Stable benchmark dataset. 1 million ratings from 6000 users on 4000 movies. Released 2/2003.

Build a RBM using this dataset to predict whether a particular user will like a movie or not. This data set contains 10000054 ratings and 95580 tags applied to 10681 movies by 71567 users of the online movie recommender service. Users were selected at random for inclusion. All users selected had rated at least 20 movies. Unlike previous MovieLens data sets, no demographic information is included. Each user is represented by an id, and no other information is provided. The data are contained in three files, movies.dat, ratings.dat and tags.dat. Also included are scripts for generating subsets of the data to support five-fold cross-validation of rating predictions.

User Ids Movielens users were selected at random for inclusion. Their ids have been anonymized.

Users were selected separately for inclusion in the ratings and tags data sets, which implies that user ids may appear in one set but not the other.

The anonymized values are consistent between the ratings and tags data files. That is, user id n, if it appears in both files, refers to the same real MovieLens user.

Ratings Data File Structure All ratings are contained in the file ratings.dat. Each line of this file represents one rating of one movie by one user, and has the following format:

UserID::MovieID::Rating::Timestamp

The lines within this file are ordered first by UserID, then, within user, by MovieID.

Ratings are made on a 5-star scale, with half-star increments.

Timestamps represent seconds since midnight Coordinated Universal Time (UTC) of January 1, 1970.

Tags Data File Structure All tags are contained in the file tags.dat. Each line of this file represents one tag applied to one movie by one user, and has the following format:

UserID::MovieID::Tag::Timestamp

The lines within this file are ordered first by UserID, then, within user, by MovieID.

Tags are user generated metadata about movies. Each tag is typically a single word, or short phrase. The meaning, value and purpose of a particular tag is determined by each user.

Timestamps represent seconds since midnight Coordinated Universal Time (UTC) of January 1, 1970.

Movies Data File Structure Movie information is contained in the file movies.dat. Each line of this file represents one movie, and has the following format:

MovieID::Title::Genres

MovieID is the real MovieLens id.

Movie titles, by policy, should be entered identically to those found in IMDB, including year of release. However, they are entered manually, so errors and inconsistencies may exist.

Genres are a pipe-separated list, and are selected from the following:

Action Adventure Animation Children's Comedy Crime Documentary Drama Fantasy Film-Noir Horror Musical Mystery Romance Sci-Fi Thriller War Western

Dataset using TMDB API, where there's 10,000 rows of data of movie's name, description, and genre.'

Potential use cases for this dataset:

1. Movie Genre Classification: The dataset can be used to train machine learning models for automatically predicting or classifying movie genres based on textual information such as overviews. This can help in building recommendation systems, genre-based search engines, or genre-based movie recommendation algorithms.

2. Genre-based Movie Analysis: Researchers or movie enthusiasts can analyze the dataset to gain insights into the distribution of different genres, identify trends, or study genre-specific characteristics. For example, they can explore the popularity of specific genres over time or analyze the relationships between different genres.

3. Content Recommendation: The dataset can be utilized to recommend movies to users based on their genre preferences. By understanding the genre composition of movies and users' past preferences, recommendation systems can suggest relevant movies that align with users' tastes.

4. Genre-specific Audience Targeting: Movie studios and production companies can use the dataset to understand the demographics and preferences of different genre-specific audiences. This information can assist in targeting specific genres to particular audience segments during marketing and distribution campaigns.

5. Sentiment Analysis: Researchers can use the dataset's overviews to perform sentiment analysis to understand the general sentiment associated with different genres. This can provide insights into how viewers perceive and respond to different movie genres.

By leveraging the columns in your "Movie Genre Detection" dataset, you can explore various aspects of movie genres, automate genre classification, offer personalized movie recommendations, and gain valuable insights into audience preferences and sentiments.

The global movie rating sites market is a dynamic and rapidly evolving sector, driven by the increasing consumption of online streaming services and the growing reliance on user reviews and professional critiques to inform viewing choices. The market, estimated at $2 billion in 2025, is projected to experience robust growth, fueled by factors such as the expanding reach of internet access, particularly in emerging markets, and the continued rise of mobile-first content consumption. Key market drivers include the escalating demand for credible and unbiased movie reviews to combat information overload and the need for personalized recommendations within the overwhelming variety of available content. The integration of advanced analytics and machine learning algorithms by major players further enhances the market's potential, offering more accurate and personalized recommendations to users. Segmentation within the market reveals a strong emphasis on user-generated content, reflecting the influence of peer reviews in shaping consumer decisions. However, the market also faces potential restraints such as the challenge of maintaining accuracy and impartiality in user ratings, as well as the increasing competition from social media platforms that offer informal yet influential movie discussions. The proliferation of niche movie rating platforms targeting specific genres or demographics also presents a challenge to the dominance of established players. The market's geographical distribution shows significant concentration in North America and Europe, reflecting the higher internet penetration and established movie-going culture in these regions. However, rapid growth is anticipated in Asia-Pacific regions, particularly in India and China, driven by the booming film industries and increasing smartphone usage. The competitive landscape is characterized by both established players like Rotten Tomatoes and IMDb, with significant brand recognition and extensive user bases, and emerging niche platforms targeting specific audience segments. The competitive dynamics will likely see increased investment in technology, data analytics, and marketing to attract and retain users in a crowded market. Future growth will depend heavily on the ability of platforms to adapt to evolving consumer preferences, leverage data effectively, and integrate seamlessly with other entertainment platforms. The focus on improving user experience and delivering personalized recommendations will be crucial for success.

This statistic shows the share of adults who read reviews before watching a movie in the United States as of August 2018, broken down by age group. The findings show that 12 percent of respondents aged between 45 and 54 years old said they always read movie reviews before seeing a movie, the largest share amongst all age groups surveyed by the source. Interestingly, the share of respondents who said that they sometimes read a film review before viewing the film is the same for 18 to 24 year olds and those ages 55 or above.

Online Movie Tickets Market Outlook

The global online movie tickets market size was valued at approximately USD 21.6 billion in 2023 and is projected to reach around USD 43.8 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 8.2% during the forecast period from 2024 to 2032. This growth is primarily driven by the increasing penetration of smartphones and the internet, along with the growing preference for the convenience of online ticket booking.

One of the most significant growth factors for the online movie tickets market is the widespread adoption of digital platforms. As more consumers gain access to high-speed internet and smartphones, the ease of booking movie tickets online has become a crucial convenience factor. This digital shift is further augmented by strategic partnerships between online ticketing platforms and film production houses, ensuring that consumers can access a wide range of movies upon their release. Furthermore, the increasing number of cinema halls and multiplexes across urban and semi-urban areas is boosting the demand for online ticket booking services.

Another key driver is the integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) into online ticketing platforms. These technologies enhance user experience by providing personalized movie recommendations and facilitating seamless booking processes. Additionally, AI and ML are being used to optimize pricing strategies and manage ticket inventories efficiently, which in turn attracts more consumers to use online platforms for their movie ticket purchases. The growing emphasis on user-friendly interfaces and secure payment gateways also contributes significantly to the market's expansion.

The increasing popularity of diverse movie genres and international cinema is also contributing to market growth. Consumers now have the flexibility to book tickets for a wide variety of movies ranging from action and comedy to drama and horror, catering to different demographic segments. The ability to access a broad selection of movies on a single platform increases the attractiveness of online booking services. Moreover, special offers and discounts provided by online ticketing platforms act as significant incentives for consumers, thereby driving market growth.

From a regional perspective, North America continues to dominate the online movie tickets market due to the high penetration of digital technologies and a robust cinema industry. However, markets in the Asia Pacific region are expected to witness the highest growth rate during the forecast period, fueled by increasing urbanization, rising disposable incomes, and expanding internet user base. Countries such as China, India, and Japan are at the forefront of this regional growth, benefiting from a burgeoning middle class and a young population that is tech-savvy and cinema enthusiastic.

Platform Analysis

The online movie tickets market is segmented by platform into mobile apps and websites. Mobile apps have gained significant traction over recent years due to the widespread use of smartphones. Consumers prefer mobile apps for their convenience, user-friendly interfaces, and the ability to book tickets on-the-go. Mobile apps often offer additional features such as push notifications for upcoming movie releases, exclusive discounts, and loyalty rewards, which further enhance user engagement. Companies are continuously innovating to improve app functionalities, incorporating voice search capabilities and AI-driven recommendations.

On the other hand, websites continue to be a vital platform for online movie ticket bookings, particularly amongst older demographics who may prefer larger screens and find it easier to navigate through websites. Websites offer comprehensive information regarding movie showtimes, trailers, reviews, and seat availability, which helps users make informed decisions. The integration of secure payment gateways and multiple payment options on these websites also ensures a smooth and secure transaction process, making them a reliable choice for many consumers.

Both platforms have their unique advantages, and the choice between them often depends on user preferences and demographic factors. However, companies in the online movie tickets market are increasingly adopting an omnichannel approach, providing seamless integration between their mobile apps and websites. This strategy ensures that users can switch between platforms without any loss of functionality or data, enhancing the overall user experience. The synergy betwee

IMDB Movie Metadata Multivariate Analysis

Exploring the Impact of Types, Ratings, and Genres on Audience Engagement

By Addi Ait-Mlouk [source]

About this dataset

This amazing IMDB movie metadata dataset has everything you need to uncover the secrets of cinema! Featuring a comprehensive set of variables that range from budget, revenue, and cast size to title types and genres - it holds the keys for unlocking lucrative insights about your favorite films. This dataset contains an array of information on over 10,000 film titles from 6 different countries (USA, UK, Germany, Canada, India and Japan). Its detailed columns include Movie ID (unique identifier for each film), Title Type (TV Series/Movie/Video), Production Budget & Revenue figures in USD along with a breakdown into Country-specific Currency Units such as EUR or GBP. Additionally, each entry features the Primary Genre Category it was classified under along with secondary Genres if applicable. Finally this collection includes text fields giving insight into plot keywords as well as Cast & Crew credits including names of Actors/Actresses in main roles plus other important personnel working on set such as Directors or Writers. Together all these features create an invaluable resource suitable for detailed analysis aiming to understand movie trends over time – How budgets compare across nations? What genres are doing better internationally all these fascinating questions addressed by this incredible dataset!

More Datasets

For more datasets, click here.

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How to use the dataset

This dataset contain 2000 films from IMDb, containing basic metadata on the films such as genre, budget and gross box office performance. It has a variety of columns which provide valuable insights when attempting to do multivariate analysis on this data. The data is divided into metacritic score (which is a measure of critics ratings), budget (in dollars), revenue (in dollars) and eight other stats related to movies

• Metacritic Score: A metric based on critical reviews ranging from 0 - 100 indicating how well the movie was reviewed by critics.
• Budget: Total budget in US dollars for each movie. This can be used to better understand why certain films are successful or not so successful when analyzing against other metrics in the dataset
• Revenue: Total revenue earned in US dollars for each movie at Box Office and outside digital downloads/streams & TV airplay etc… we could use this to calculate revenue per budget for movies as well as success rates of certain genres over others given higher budgets
• Release Date: The release date given in string format mainly used for understanding seasonality effects between various releases in different times throughout the year e.g summer vs winter releases
  5 .Runtime: Length of time that a film plays for expressed in minutes could be useful measuring watchability/ engagement potential or correlations with average ticket prices at theatre pricing timeframes(matinees vs evening shows)

6 Genres : Genres associated with a particular film e.g comedy ,drama , horror etc … Value information taken from freedb collection created by IMDB,these categories can also help further narrow down what people like and disliked about certain films or companies producing them

7 Directors : Each directors name usually one director per film which might give us some insight into directing decisions made which have an impact on box-office performance

8 Writers : Names of writers associated with a particular project same reasoning applies here that making different decisions around who write content reflects audience response

9 Actors/Actresses : Names of some key actors associate with major roles like leads/support takes who worked on stories will give more insight into why viewers preferred them OVER OTHER PROJECTS THEY MAY HAVE BEEN ASSOCIATED WITH along their career paths

10 Countries apart from USA origin countries are identified Here since many american productions try new approaches keeping an eye out may point us towards effective techniques being implemented abroad that have potential application to American markets ADR

Research Ideas

• Creating movie recommendation systems based on user preferences: This dataset can be used to uncover patterns in user movie watching habits and preferences by factoring in multiple variables such as genre, release year, director, cast, popularity/ratings etc., and recommending similar movies accordingly.
• Cost vs Profit predictive models: This dataset can be utilized to construct a p...

Datasets Structure

This folder contains the multimodal features of the three state-of-the-art we have extended (`MovieLens-1M`, `DBbook`, `Last.FM-2K`).

For each folder, we provide both the interaction data in the original format (in the folder `interaction_data`) and the multimodal features in several formats, based on the needs (in the `multimodal_data` folder).

In the following, we provide all the information needed to work with such data. Note that, although some dataset-specif details mght change, the general strucuture is common to all the three datasets.

Dataset statistics

Interaction data

The `interaction_data` contains the interaction data provided in the original version of each datasets. We prefer sharing the original version so that each one can pre-process it in the way they prefer (e.g., apply a certain k-core filtering, adapt the task to sequential recommendation by exploiting temporal information - when available -, and so on).

ML1M

In `MovieLens-1M`, interaction data includes user information (`users.dat`), movie information (`movies.dat`), and user ratings (`ratings.dat`); in order to work with this data, we suggest to read those files with the `pandas` python library, by using the `ISO-8859-1` encoding (if using other encoding, like `utf-8`, the reading will raise an error); the default separation character sequence is `::`. For example, in order to read ratings and movie information, one should use:

ratings = pd.read_csv('interaction_data/ratings.dat', sep='::', names=['user', 'item', 'rating', 'timestamp'])
movies = pd.read_csv('interaction_data/movies.dat', sep='::', names=['id', 'name', 'genres'], encoding='ISO-8859-1')

DBbook

In `DBbook`, interaction data includes training and testing data (already split, as in the original version); unfortunately, such version cannot be download anymore as the original web page is no longer accessible; using tools like [waybackmachines, it possible to access that page and download some files, but only the training data is available in the backups that have been made, while test data is not obtaibale.
For these reasons, we considered the version of the dataset that have been used in other works listed below and reachable at the public repository of our SWAP Research Group:
- https://dl.acm.org/doi/abs/10.1145/3523227.3551484
- https://dl.acm.org/doi/abs/10.1145/3565472.3592965
- https://dl.acm.org/doi/abs/10.1145/3627043.3659548
- https://link.springer.com/article/10.1007/s11257-024-09417-x

This way, we have been able to reconstruct the full verison of this dataset.
Similarly to `MovieLens-1M`, interaction data contains user ratings in the `train.tsv` and `test.tsv` files, and book information in the `DBbook_Items_DBpedia_mapping.tsv` file.

We suggest to load such data using `pandas` as follows:

train = pd.read_csv('interaction_data/train.tsv', sep='\t', names=['userID', 'itemID', 'rating'])
test = pd.read_csv('interaction_data/test.tsv', sep='\t', names=['userID', 'itemID', 'rating'])
books = pd.read_csv('interaction_data/DBbook_Items_DBpedia_mapping.tsv', sep='\t')

Last.FM-2K

In `LFM2K`, interaction data is encoded in the `user_artists.dat` file; this file encodes the listening counts for each pair (user,item) available (from this information, it is possible to derive the user ratings); the file `artist_info` encodes information assiciated to the artists, including the name of the artist, the URL of the associated Last.FM resource, and the link to the image (not available anymore); the file `tags.dat` contains the set of all the possible tags users attributed to artists, while all the tags attributed to specific artists is encoded in the `user_taggedartists.dat` file (the `user_taggedartists-timestamps` contains, in addition, the timestamp of the attribution).

In order to read data, we suggest to use `pandas` as follows:

interactions = pd.read_csv('original_data/user_artists.dat', sep='\t')
artist_info = pd.read_csv('original_data/artists.dat', sep='\t')
usertag = pd.read_csv('original_data/user_taggedartists-timestamps.dat', sep='\t')
tags = pd.read_csv('original_data/tags.dat', sep='\t', encoding='latin-1')

Multimodal data

Each dataset is also provided with with multimodal data, in the `multimodal_features` folder. In this folder, we include the data source data we considered (plain text and links to image/audio/video files), with the pre-trained multimodal features.

Here is the coverage of multimodal information w.r.t. the datasets considered:

• As depicted in the table, for `ML1M` we have gathered movie plots (text), movie posters (images), and movie trailers (for audio and video); in the `movielens_1m/multimodal_features` folder, we provide an extended mapping named `ml1m_full_extended_mapping`, in which we report which are the links to download `covers` and `trailers`, while `text` is available in the `text_ml1m.tsv` file.
• For `DBbook`, we have gathered book abstracts (text) and book covers (images); in the `dbbook/multimodal_features` folder, we provide an extended mapping named `full_extended_dbbook_img_links.tsv`, in which we report which are the links to download the `book covers`, while `text` is available in the `dbbook_text.tsv` file.
• For `LFM2K`, we have gathered artist tags (text), the top-5 most popular album covers (images), and the top-5 most popular audio songs (audio); in the `lfm2k/multimodal_features.tsv` folder, we report extended mappings, named `lfm2k_song_extended_mapping.tsv` and `lfm2k_covers_extended_mapping.tsv`, tha encode the top-5 most popular `songs` and `album covers` for each artist, respectively; on the other hand, the `lfm2k_text.tsv` encode the `text` we considered, obtained from the user tags.

With this information, anyone can donwload the raw features and use them in their recommendation scenario; in our case, to carry out our experiments, we considered the following state-of-the-art multimodal encoders:

• Text: we considered `MiniLM` and `MPNET` (for `ML1M`, `DBbook`, and `LFM2K`)
• Image: we considered `ResNet152`, `VGG`, `ViT_AVG`, `ViT_CLS` (for `ML1M`, `DBbook`, and `LFM2K`)
• Audio: we considered `VGGish` and `Whisper` (for `ML1M` and `LFM2K`)
• Video: we considered `I3D` and `R(2+1)D` (for `ML1M`)

The resulting features have been dumped as `dict` (`item_id` -> `np.float32` embedding) in a pickle `.pkl` file, that can be found in the `multimodal_features/dict` folders (one for each dataset); moreover, to avoid any error in reading such files, we have also saved the embeddings in `.json` files, in the `multimodal_features/json` folders (one for each dataset); finally, to reproduce our experiments, we report the same data as `.npy` files (as required by `MMRec`), that can be found in the `multimodal_features/npy` folders (one for each dataset).

Encode multimodal features

In order to learn the multimodal features by exploiting the encoders we considered in our experimental analysis, please refer to the GitHub reporisory

According to a study held in June 2020, just 14 percent of adults said that they strongly preferred seeing a movie for the first time in a theater, and 36 percent said that they would much rather stream the film at home than visit a cinema. Preferences for watching a new release in a cinema instead of via a streaming service in the United States changed significantly between 2018 and 2020, signaling a shift in consumer behavior and potentially a risk for movie theaters in the country. Also important to note is the effect of the coronavirus on consumer confidence. There was a drop in the share of movie fans willing to visit cinemas between March and June 2020, likely the result of consumers fearing the risk of infection and feeling more comfortable viewing movies in the safety of their own home.

Streaming Movie Device for TV Market Outlook

The global market size for streaming movie devices for TVs was valued at approximately USD 15.3 billion in 2023, and it is projected to reach around USD 34.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.4%. This robust growth is driven by the increasing consumer demand for on-demand entertainment, the proliferation of high-speed internet, and technological advancements in streaming devices.

One of the primary growth factors is the surging demand for on-demand content. Consumers are increasingly moving away from traditional cable TV subscriptions to flexible streaming services that offer a wide variety of content. Streaming devices for TVs have become essential as they allow users to access platforms like Netflix, Amazon Prime, Disney+, and more, directly on their television screens. The ease of use, coupled with affordable prices of these devices, is driving their adoption across various demographics.

Technological advancements and innovations are also significant growth drivers. Modern streaming devices now offer enhanced features such as 4K Ultra HD, HDR support, and voice control, providing a superior viewing experience. Additionally, the integration of artificial intelligence and machine learning in these devices is enabling personalized content recommendations, which significantly enhance user engagement and satisfaction. Companies are continually investing in R&D to bring more advanced and user-friendly devices to the market, further fuelling the market growth.

The increasing penetration of high-speed internet and the expansion of Wi-Fi infrastructure globally are also crucial factors. As more regions gain access to reliable and fast internet connections, the potential user base for streaming devices expands. This is particularly evident in developing regions where internet penetration is rapidly increasing. The advent of 5G technology is expected to further boost the market by enabling faster and more reliable streaming capabilities.

Regionally, North America currently holds the largest market share due to the high adoption rates of streaming devices and the presence of key market players. However, the Asia Pacific region is expected to witness the highest growth rate over the forecast period. Factors such as increasing disposable incomes, rapid urbanization, and a growing middle-class population are contributing to the market's expansion in this region. The rising popularity of local and international streaming platforms is also driving demand in countries like India, China, and Japan.

Product Type Analysis

The product type segment of the streaming movie device for TV market is categorized into streaming sticks, set-top boxes, smart TVs, and gaming consoles. Streaming sticks are among the most popular choices due to their compact size, affordability, and ease of use. They are plug-and-play devices that can be easily connected to any TV with an HDMI port, making them highly versatile. Brands like Amazon’s Fire Stick and Roku have garnered significant market shares due to their competitive pricing and extensive content libraries.

Set-top boxes represent another significant segment. These devices often offer more features and capabilities compared to streaming sticks, such as higher storage capacity, better processing power, and more extensive connectivity options. Set-top boxes from companies like Apple, Google, and Nvidia are favored by consumers who seek a more robust and feature-rich streaming experience. The ability to support gaming, smart home integration, and advanced video formats like 4K HDR makes set-top boxes a compelling option for tech enthusiasts.

Smart TVs, which come with built-in streaming capabilities, are also gaining popularity. As more consumers look for all-in-one solutions, smart TVs offer a seamless and integrated streaming experience without the need for additional devices. Brands like Samsung, LG, and Sony are leading this market segment by incorporating advanced features such as voice assistants, AI-driven recommendations, and enhanced picture quality. The convenience of having streaming apps pre-installed is a significant selling point for smart TVs.

Gaming consoles, while primarily designed for gaming, also serve as powerful streaming devices. Consoles like the PlaySt

Artificial intelligence is making its way into various aspects of entertainment, with television and movie viewing being no exception. A late-2024 survey revealed that U.S. consumers showed significant interest in AI-powered features for their viewing experience. Consumers were especially interested in AI analyzing habits across all apps to receive better recommendations, with 35 percent being very and 41 percent somewhat interested in this feature. Less popular were AI chatbots integrated into their connected TV devices.

Digital Film Distribution Market Outlook

The global digital film distribution market size is projected to grow from USD 10.5 billion in 2023 to USD 35.0 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 14.2% during the forecast period. The primary growth factors driving this market include the increasing penetration of high-speed internet, the widespread adoption of smart devices, and the growing consumer preference for on-demand content. These factors are contributing significantly to the expansion and transformation of the digital film distribution landscape.

One of the key drivers for the growth of the digital film distribution market is the rapid advancement in internet infrastructure. The proliferation of high-speed internet has made it easier for consumers to access digital content seamlessly. This is coupled with the increasing use of smartphones, tablets, and smart TVs, which has further facilitated the shift from traditional media to digital platforms. The convenience offered by digital distribution, such as the ability to watch content anytime and anywhere, has led to a significant increase in consumer demand, thereby fueling market growth.

Another crucial growth factor is the rise in consumer preference for on-demand content. Subscription-based services like Netflix, Amazon Prime Video, and Disney+ have revolutionized the way people consume media. These platforms offer a vast library of movies and TV shows that can be accessed at the consumer's convenience, contributing to the shift away from traditional cable and satellite TV. This trend is particularly prominent among younger demographics, who are more inclined towards digital consumption, thus driving the market forward.

The increasing investments by major players in the market to enhance their digital content libraries and improve user experience are also propelling the market growth. Companies are focusing on acquiring exclusive rights to popular content, developing original productions, and leveraging advanced technologies like artificial intelligence for personalized recommendations. These initiatives are not only attracting new subscribers but also retaining existing ones, thereby boosting the overall market size. Furthermore, the strategic partnerships and collaborations among content creators, distributors, and technology providers are creating a robust ecosystem for digital film distribution.

Regionally, North America holds a significant share of the digital film distribution market, driven by the high adoption rate of digital platforms and strong internet infrastructure. However, the Asia Pacific region is expected to witness the highest growth during the forecast period, owing to the rapid digitization, increasing smartphone penetration, and growing middle-class population with disposable income. The shift in consumer behavior towards digital content in emerging economies like India and China is creating lucrative opportunities for market players.

Type Analysis

When segmented by type, the digital film distribution market can be classified into Transactional Video on Demand (TVOD), Subscription Video on Demand (SVOD), and Advertising Video on Demand (AVOD). TVOD services allow consumers to pay for individual pieces of content, such as movies or episodes, which they can either rent or purchase. This model is gaining traction due to its flexibility and the ability to access exclusive content without a subscription commitment. The growing demand for premium and recent releases is driving the TVOD segment's growth.

SVOD services, including giants like Netflix, Amazon Prime, and Disney+, offer unlimited access to a library of content for a monthly or annual subscription fee. This model has become extremely popular due to the vast array of available content and the convenience it offers. The increasing trend of cord-cutting, where consumers are moving away from traditional cable services to digital subscriptions, is significantly boosting the SVOD market. The continuous addition of new and original content by these platforms is attracting more subscribers, thereby driving the segment’s growth.

AVOD services, such as YouTube and Hulu, provide free access to content supported by advertisements. This model is appealing to consumers who prefer not to pay for subscriptions but are willing to watch advertisements in exchange for free content. The AVOD segment is witnessing substantial growth due to its accessibility and the growing preference for ad-supported free content. Advertisers are also keen on this model as it allows them t

Online Movie Ticketing Service Market Outlook

The global online movie ticketing service market size was valued at approximately USD 21.9 billion in 2023 and is projected to reach around USD 45.0 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.4% during the forecast period. This impressive growth can be attributed to the increasing penetration of smartphones and internet connectivity, coupled with the rising preference for online services due to their convenience and efficiency.

One of the key growth factors driving the online movie ticketing service market is the increasing adoption of digital platforms for entertainment purposes. As more consumers shift from traditional methods of purchasing tickets to online platforms, the market experiences significant growth. The convenience of booking tickets online, avoiding long queues, and the ability to choose preferred seats are compelling factors that attract consumers to these digital platforms. Additionally, the integration of various payment methods and promotional offers further boosts the market's attractiveness.

Another major growth driver is the burgeoning popularity of movie-going as a primary leisure activity, especially post the COVID-19 pandemic. Despite initial setbacks during the pandemic, the market has witnessed a robust recovery with cinemas reopening and audiences returning in large numbers. The pent-up demand for entertainment and the release of blockbuster movies have catalyzed the market's resurgence. Furthermore, the increasing number of multiplexes and cinema chains expanding their online presence has further strengthened the market.

The rapid advancements in technology also play a crucial role in propelling the online movie ticketing service market. The advent of augmented reality (AR) and virtual reality (VR) in the booking process, personalized recommendations through AI algorithms, and seamless integration with social media platforms enhance the user experience. These technological innovations not only make the booking process more engaging but also drive consumer loyalty and repeat usage.

Cinema Point of Sale (POS) Solutions are becoming increasingly integral to the online movie ticketing service market. These solutions streamline the ticket purchasing process by integrating with online platforms, allowing for seamless transactions both online and at the cinema. By offering features such as real-time seat availability, dynamic pricing, and integration with loyalty programs, Cinema POS Solutions enhance the overall customer experience. They also provide valuable data insights for cinema operators, helping them optimize operations and marketing strategies. As cinemas continue to embrace digital transformation, the adoption of advanced POS systems is expected to rise, further supporting the growth of the online movie ticketing market.

Regionally, North America holds a significant share in the global online movie ticketing service market, attributed to the high internet penetration rate, well-established cinema industry, and tech-savvy population. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The increasing urbanization, growing middle-class population, and rapid adoption of smartphones in countries like China and India are major contributing factors. The regionÂ’s burgeoning entertainment industry, coupled with strategic partnerships and investments by key players, further solidifies its growth potential.

Platform Analysis

The online movie ticketing service market can be segmented based on the platform into mobile app and website. The mobile app segment holds a substantial share of the market and is expected to exhibit significant growth during the forecast period. The increasing adoption of smartphones and the convenience of mobile applications have revolutionized the ticket booking experience. Mobile apps offer a user-friendly interface, personalized recommendations, and seamless integration with payment gateways, making them a preferred choice for consumers. Additionally, the push notifications and exclusive offers available through apps enhance user engagement and drive the segment's growth.

On the other hand, the website segment also continues to play a vital role in the online movie ticketing service market. While mobile apps cater to the on-the-go audience, websites serve a broader demographic, including those who pr

The global streaming movies app market is experiencing robust growth, driven by increasing internet penetration, the affordability of smartphones, and a rising preference for on-demand entertainment. The market size in 2025 is estimated at $85 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant expansion is fueled by several key trends, including the proliferation of original content from streaming platforms, the rise of subscription-based models offering diverse content libraries, and increasing integration with smart TVs and other connected devices. The market segmentation reveals a strong preference for Android and iOS systems, with a near-even split between personal and family application usage. Competition is fierce, with established players like Netflix, Amazon Prime Video, and Disney+ vying for market share alongside emerging platforms and niche providers. While the market faces constraints such as data caps, internet accessibility issues in certain regions, and increasing competition leading to price wars, the overall outlook remains positive due to the ongoing shift towards digital entertainment consumption and the continuous innovation within the streaming landscape. The market’s regional distribution shows significant concentration in North America and Europe, driven by higher disposable incomes and advanced digital infrastructure. However, rapid growth is projected in Asia-Pacific regions like India and China due to increasing smartphone penetration and a burgeoning young population with high entertainment consumption habits. To maintain competitiveness, companies are investing heavily in personalized recommendations, improved user interfaces, and the expansion of their content libraries to cater to diverse regional preferences and tastes. Furthermore, the integration of advanced technologies such as Artificial Intelligence (AI) for content recommendation and Virtual Reality (VR) for immersive viewing experiences are anticipated to further fuel market expansion in the coming years. The continued evolution of streaming technologies, including 4K and 8K resolution streaming, promises a significant impact on the market's future growth trajectory.

Context

The datasets describe ratings and free-text tagging activities from MovieLens, a movie recommendation service. It contains 20000263 ratings and 465564 tag applications across 27278 movies. These data were created by 138493 users between January 09, 1995 and March 31, 2015. This dataset was generated on October 17, 2016.

Users were selected at random for inclusion. All selected users had rated at least 20 movies.

Content

No demographic information is included. Each user is represented by an id, and no other information is provided.

The data are contained in six files.

tag.csv that contains tags applied to movies by users:

• userId

• movieId

• tag

• timestamp

rating.csv that contains ratings of movies by users:

• userId

• movieId

• rating

• timestamp

movie.csv that contains movie information:

• movieId

• title

• genres

link.csv that contains identifiers that can be used to link to other sources:

• movieId

• imdbId

• tmbdId

genome_scores.csv that contains movie-tag relevance data:

• movieId

• tagId

• relevance

genome_tags.csv that contains tag descriptions:

• tagId

• tag

Acknowledgements

The original datasets can be found here. To acknowledge use of the dataset in publications, please cite the following paper:

F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages. DOI=http://dx.doi.org/10.1145/2827872

Inspiration

Some ideas worth exploring:

• Which genres receive the highest ratings? How does this change over time?

• Determine the temporal trends in the genres/tagging activity of the movies released

The Permanent Traffic Sign Film market is a crucial segment in the broader industrial landscape, dedicated to enhancing road safety and transportation efficiency through high-quality signage solutions. This specialized film is designed for use in traffic signs, offering durability and visibility under various enviro

Romance Film and TV Show Market Outlook

The global romance film and TV show market size was valued at approximately $30 billion in 2023 and is projected to reach around $47 billion by 2032, growing at a compound annual growth rate (CAGR) of 5.0% from 2024 to 2032. This growth is driven by a range of factors including rising consumer demand for escapism, increased accessibility through digital streaming platforms, and evolving cultural narratives that are expanding the genre's appeal. In an increasingly digital age, romance media offers a comforting and often idealized reflection of personal relationships, making it a popular choice among diverse audiences worldwide. The ongoing evolution in content delivery and the proliferation of platforms are expected to continue fueling the market's growth trajectory over the coming years.

One of the primary growth factors in the romance film and TV show market is the increasing penetration of streaming platforms. Services like Netflix, Amazon Prime Video, and Disney+ are not only broadening access to romantic content but are also investing heavily in original productions that cater to a global audience. This shift from traditional television and cinema to on-demand viewing aligns with changing consumer behaviors, where convenience and content diversity are paramount. Streaming platforms allow for a more personalized viewing experience, offering recommendations based on viewer preferences, which in turn fosters increased consumption of romance genres. This digital accessibility is a crucial contributor to the market's expansion, as it allows for both widespread distribution and niche targeting, giving rise to a broader spectrum of romantic narratives.

Furthermore, the cultural dynamics influencing the production and consumption of romance narratives are pivotal to the market's growth. Modern audiences are gravitating towards narratives that reflect more diverse and inclusive experiences, prompting creators to explore a variety of themes within the romance genre. This includes exploring narratives around LGBTQ+ relationships, interracial romances, and stories that challenge traditional gender roles. The industry's responsiveness to societal changes is expanding the genre's appeal beyond its traditional base, attracting a younger, more diverse audience. This inclusive storytelling not only caters to the evolving tastes of global viewers but also offers new creative avenues for filmmakers and writers, thereby sustaining the genre's relevance and popularity.

Technological advancements also play a significant role in shaping the romance film and TV show market. Enhanced production techniques, including the use of CGI, virtual reality, and augmented reality, are being integrated into romantic narratives, offering immersive experiences that captivate audiences. Moreover, the role of social media as a tool for marketing and audience engagement cannot be understated. Platforms such as Instagram, Twitter, and TikTok allow studios and content creators to engage directly with fans, gauge viewer preferences, and create buzz around upcoming releases. This direct engagement with audiences leads to more tailored content offerings, further driving consumption and market growth.

Genre Analysis

The romance genre is diverse, encompassing several sub-genres that cater to varying audience tastes and preferences. Contemporary romance remains one of the most popular segments, characterized by modern-day settings and relatable plots that resonate with today's audience. These films and shows often explore the complexities of relationships in the digital age, addressing themes such as online dating, long-distance relationships, and the impact of social media on love. The universality of contemporary romance, coupled with its adaptability to current trends, makes it a staple in both film and television. This segment's popularity is expected to continue as it evolves with societal changes, ensuring its relevance to a wide demographic.

Historical romance offers audiences an escape into different eras, often providing a rich tapestry of period-specific costumes, settings, and cultural norms. This genre appeals to viewers who are fascinated by history and the timeless nature of love stories set against the backdrop of significant historical events or periods. The allure of historical romance lies in its ability to transport viewers to times past while exploring themes that remain relatable today. Productions in this segment often require significant investment in set design and costuming, which can result in visually stunning and critically acclaimed work

Movie Streaming Service Market Outlook

The global movie streaming service market size was valued at approximately USD 72.2 billion in 2023 and is expected to reach around USD 192.1 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.5% during the forecast period. The growth of this market is fueled by the increasing penetration of high-speed internet, the rising popularity of smart devices, and the growing consumer preference for on-demand content. As more consumers look for convenience and personalized entertainment, the demand for movie streaming services has surged, creating significant opportunities for market expansion.

One of the primary growth factors for the movie streaming service market is the broad availability of high-speed internet. The proliferation of 4G and 5G networks has made it possible for users to stream high-definition content seamlessly, thereby enhancing the overall user experience. As internet accessibility continues to improve worldwide, particularly in developing regions, the market is expected to witness substantial growth. Furthermore, the ongoing digital transformation initiatives by various governments are also contributing to the expansion of internet infrastructure, thereby supporting the growth of the movie streaming service market.

Another significant factor driving the market growth is the increasing adoption of smart devices such as smartphones, tablets, and smart TVs. The convenience of accessing a vast library of movies and TV shows on personal devices has transformed consumer viewing habits. This shift towards digital consumption is further amplified by the increasing affordability and enhanced capabilities of smart devices. As these devices become more advanced and accessible, the demand for movie streaming services is expected to rise correspondingly. The integration of advanced features such as voice recognition and personalized recommendations also plays a crucial role in boosting user engagement and satisfaction.

The evolving consumer preference for on-demand and personalized content is also a crucial growth driver for the movie streaming service market. Traditional TV viewing is increasingly being replaced by streaming services due to their flexibility and vast content libraries. Consumers are now empowered to watch their favorite movies and shows anytime, anywhere, without being bound by schedules. This shift is particularly pronounced among younger generations, who are more inclined towards binge-watching and exploring diverse content. The ability of streaming platforms to offer tailored recommendations based on user preferences further enhances their appeal, contributing to market growth.

From a regional perspective, North America currently holds the largest share of the movie streaming service market, driven by high internet penetration, a large base of tech-savvy consumers, and the presence of major streaming service providers. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by the rapid adoption of smartphones, increasing internet penetration, and a growing middle-class population. The rising demand for regional and localized content is also a significant factor contributing to the market's growth in this region. Europe, Latin America, and the Middle East & Africa are also expected to experience steady growth, driven by similar factors.

Service Type Analysis

The movie streaming service market is segmented by service type into subscription-based, ad-supported, transactional video on demand (TVOD), and others. The subscription-based model is currently the most popular and widely adopted service type. This model offers users unlimited access to a vast library of content for a recurring fee, typically on a monthly or yearly basis. The subscription-based model's popularity can be attributed to its cost-effectiveness and the convenience it offers to users who prefer to binge-watch content. Major players like Netflix, Amazon Prime Video, and Disney+ dominate this segment, providing a diverse range of movies and TV shows to cater to various tastes and preferences.

The ad-supported model is another significant segment within the movie streaming service market. This model allows users to access content for free or at a lower cost in exchange for watching advertisements. The ad-supported model is particularly popular in regions with lower disposable incomes, as it provides an affordable alternative to subscription-based services. Platforms like Hulu and Peacock have successfully leveraged this model to attract a broader audie

Getting a recommendation from friends or family members was the most important reason for choosing a new movie or TV series on a streaming service in Sweden in 2019. While 29 percent of Swedes relied on recommendations from someone close to them, another 18 percent asked the internet database IMDB for advice. In comparison, eight percent found something new to watch on the streaming service itself.

Analysis of ‘Young People Survey’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/miroslavsabo/young-people-survey on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Introduction

In 2013, students of the Statistics class at "https://fses.uniba.sk/en/">FSEV UK were asked to invite their friends to participate in this survey.

• The data file (responses.csv) consists of 1010 rows and 150 columns (139 integer and 11 categorical).
• For convenience, the original variable names were shortened in the data file. See the columns.csv file if you want to match the data with the original names.
• The data contain missing values.
• The survey was presented to participants in both electronic and written form.
• The original questionnaire was in Slovak language and was later translated into English.
• All participants were of Slovakian nationality, aged between 15-30.

The variables can be split into the following groups:

• Music preferences (19 items)
• Movie preferences (12 items)
• Hobbies & interests (32 items)
• Phobias (10 items)
• Health habits (3 items)
• Personality traits, views on life, & opinions (57 items)
• Spending habits (7 items)
• Demographics (10 items)

Research questions

Many different techniques can be used to answer many questions, e.g.

• Clustering: Given the music preferences, do people make up any clusters of similar behavior?
• Hypothesis testing: Do women fear certain phenomena significantly more than men? Do the left handed people have different interests than right handed?
• Predictive modeling: Can we predict spending habits of a person from his/her interests and movie or music preferences?
• Dimension reduction: Can we describe a large number of human interests by a smaller number of latent concepts?
• Correlation analysis: Are there any connections between music and movie preferences?
• Visualization: How to effectively visualize a lot of variables in order to gain some meaningful insights from the data?
• (Multivariate) Outlier detection: Small number of participants often cheats and randomly answers the questions. Can you identify them? Hint: [Local outlier factor][1] may help.
• Missing values analysis: Are there any patterns in missing responses? What is the optimal way of imputing the values in surveys?
• Recommendations: If some of user's interests are known, can we predict the other? Or, if we know what a person listen, can we predict which kind of movies he/she might like?

Past research

• (in slovak) Sleziak, P. - Sabo, M.: Gender differences in the prevalence of specific phobias. Forum Statisticum Slovacum. 2014, Vol. 10, No. 6. [Differences (gender + whether people lived in village/town) in the prevalence of phobias.]

• Sabo, Miroslav. Multivariate Statistical Methods with Applications. Diss. Slovak University of Technology in Bratislava, 2014. [Clustering of variables (music preferences, movie preferences, phobias) + Clustering of people w.r.t. their interests.]

Questionnaire

MUSIC PREFERENCES

1. I enjoy listening to music.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
2. I prefer.: Slow paced music 1-2-3-4-5 Fast paced music (integer)
3. Dance, Disco, Funk: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
4. Folk music: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
5. Country: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
6. Classical: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
7. Musicals: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
8. Pop: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
9. Rock: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
10. Metal, Hard rock: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
11. Punk: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
12. Hip hop, Rap: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
13. Reggae, Ska: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
14. Swing, Jazz: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
15. Rock n Roll: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
16. Alternative music: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
17. Latin: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
18. Techno, Trance: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
19. Opera: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)

MOVIE PREFERENCES

1. I really enjoy watching movies.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
2. Horror movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
3. Thriller movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
4. Comedies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
5. Romantic movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
6. Sci-fi movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
7. War movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
8. Tales: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
9. Cartoons: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
10. Documentaries: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
11. Western movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
12. Action movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)

HOBBIES & INTERESTS

1. History: Not interested 1-2-3-4-5 Very interested (integer)
2. Psychology: Not interested 1-2-3-4-5 Very interested (integer)
3. Politics: Not interested 1-2-3-4-5 Very interested (integer)
4. Mathematics: Not interested 1-2-3-4-5 Very interested (integer)
5. Physics: Not interested 1-2-3-4-5 Very interested (integer)
6. Internet: Not interested 1-2-3-4-5 Very interested (integer)
7. PC Software, Hardware: Not interested 1-2-3-4-5 Very interested (integer)
8. Economy, Management: Not interested 1-2-3-4-5 Very interested (integer)
9. Biology: Not interested 1-2-3-4-5 Very interested (integer)
10. Chemistry: Not interested 1-2-3-4-5 Very interested (integer)
11. Poetry reading: Not interested 1-2-3-4-5 Very interested (integer)
12. Geography: Not interested 1-2-3-4-5 Very interested (integer)
13. Foreign languages: Not interested 1-2-3-4-5 Very interested (integer)
14. Medicine: Not interested 1-2-3-4-5 Very interested (integer)
15. Law: Not interested 1-2-3-4-5 Very interested (integer)
16. Cars: Not interested 1-2-3-4-5 Very interested (integer)
17. Art: Not interested 1-2-3-4-5 Very interested (integer)
18. Religion: Not interested 1-2-3-4-5 Very interested (integer)
19. Outdoor activities: Not interested 1-2-3-4-5 Very interested (integer)
20. Dancing: Not interested 1-2-3-4-5 Very interested (integer)
21. Playing musical instruments: Not interested 1-2-3-4-5 Very interested (integer)
22. Poetry writing: Not interested 1-2-3-4-5 Very interested (integer)
23. Sport and leisure activities: Not interested 1-2-3-4-5 Very interested (integer)
24. Sport at competitive level: Not interested 1-2-3-4-5 Very interested (integer)
25. Gardening: Not interested 1-2-3-4-5 Very interested (integer)
26. Celebrity lifestyle: Not interested 1-2-3-4-5 Very interested (integer)
27. Shopping: Not interested 1-2-3-4-5 Very interested (integer)
28. Science and technology: Not interested 1-2-3-4-5 Very interested (integer)
29. Theatre: Not interested 1-2-3-4-5 Very interested (integer)
30. Socializing: Not interested 1-2-3-4-5 Very interested (integer)
31. Adrenaline sports: Not interested 1-2-3-4-5 Very interested (integer)
32. Pets: Not interested 1-2-3-4-5 Very interested (integer)

PHOBIAS

1. Flying: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
2. Thunder, lightning: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
3. Darkness: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
4. Heights: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
5. Spiders: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
6. Snakes: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
7. Rats, mice: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
8. Ageing: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
9. Dangerous dogs: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
10. Public speaking: Not afraid at all 1-2-3-4-5 Very afraid of (integer)

HEALTH HABITS

1. Smoking habits: Never smoked - Tried smoking - Former smoker - Current smoker (categorical)
2. Drinking: Never - Social drinker - Drink a lot (categorical)
3. I live a very healthy lifestyle.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)

PERSONALITY TRAITS, VIEWS ON LIFE & OPINIONS

1. I take notice of what goes on around me.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
2. I try to do tasks as soon as possible and not leave them until last minute.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
3. I always make a list so I don't forget anything.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
4. I often study or work even in my spare time.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
5. I look at things from all different angles before I go ahead.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
6. I believe that bad people will suffer one day and good people will be rewarded.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
7. I am reliable at work and always complete all tasks given to me.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
8. I always keep my promises.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
9. **I can fall for someone very quickly and then