Review: This column contains textual reviews provided by customers. Each row represents a specific customer's opinion or feedback about their experience at the restaurant. The reviews may include positive or negative comments about various aspects such as food, service, ambiance, cleanliness, etc.

Liked: This column represents the rating given by customers to indicate whether they liked or disliked their experience. The values in this column are numerical, with 1.0 indicating that the customer liked the restaurant and 0.0 indicating that the customer did not like it. The "Liked" column serves as the target variable in a binary classification task, where the goal could be to predict whether future customers will like or dislike the restaurant based on their reviews.

The dataset contains 2221 rows and 2 columns

2220 rows × 2 columns

BDFoodReview is a large-scale dataset containing 334,119 restaurant reviews collected from "Foodpanda Bangladesh". The dataset includes customer reviews in mixed languages (Bangla, English, and Banglish), translated into English, along with their corresponding ratings and sentiment labels.

Dataset Statistics Total Reviews: 334,119 Features/Columns: 19

Potential Applications Sentiment Analysis Restaurant Review Classification Customer Satisfaction Analysis Opinion Mining Natural Language Processing Research Food Service Industry Analysis

Context

This is a dataset for practicing Sentiment Analysis, Text analytics and classification etc. Dataset will be updated on regular basis by scrapping reviews from websites .

Content

Analyse your NLP skills and make some amazing notebooks and perform some text classification . The reviews can be positive or negative/liked or disliked . Positive Review : 1 Negative Review : 0

The file contains four public datasets, which are taken from user comments on Laptop and Restaurant. Each dataset is composed of review, aspect words composed of one or more words, and the sentiment polarity of the aspect word. The sentiment polarity is composed of Positive, Neutral, and Negative.

The Kaggle sentiment analysis competition dataset contains unlabeled restaurant reviews used to supplement the labeled SemEval dataset for improved performance in sentiment analysis.

Google Restaurant Reviews Dataset

  Dataset Summary

This dataset contains 140 original restaurant reviews, each with an associated star rating (from 1 to 5). It is designed for supervised machine learning tasks, specifically text classification and sentiment analysis. The dataset is composed of two main splits:

original: 140 reviews authored by a large language model (Google's Gemini) to serve as a clean, foundational dataset. augmented: 1,120 additional reviews… See the full description on the dataset page: https://huggingface.co/datasets/zacCMU/2025-24679-review-text-dataset.

Restaurant Reviews CZ ABSA - 2.15k reviews with their related target and category

The work done is described in the paper: https://doi.org/10.13053/CyS-20-3-2469

Datasets of Tripadvisor reviews by UK residents of UK hotels and restaurants, together with the user's rating of the hotel.Datasets are split by:Hotel star level (2, 3, 4 or all[mixed]) or Restaurant;Reviewer gender (M=male-authored reviews; F=female-authored reviews; MF=equal numbers of male and female authored reviews for each rating level);Number of texts (1k, 2k, 4k, 8k, 16k, or all available)Each dataset contains equal numbers of reviews at each rating level.The reviews were selected at random from TripAdvisor.This data is from this paper:Thelwall, M. (2018). Gender bias in machine learning for sentiment analysis. Online Information Review, 42(3), 343-354. doi: 10.1108/OIR-05-2017-0152

This dataset corresponds to the paper "'Authentic and amazing': authenticity as an evaluative category in online consumer restaurant reviews" appearing in Cultural Analytics. This dataset provides the R scripts used for the preparation, analysis as well as the import of data to Sketch Engine, the ID lists of the reviews in Corpus 1, 2 and 3, as well as the authenticity lexicons used which were derived from O'Connor et. al (2017) under a CC BY 4.0 license. The IDs correspond the those in the Yelp Dataset at the time of data collection (2019).

Data

We provide you with a data set in CSV format. The data set contains food review for the restaurant

The target variable is labeled Sentiment.

Task

Create a Classification model to predict the target variable Sentiment.

1. A report - A Power point presentation
2. Any custom code you used
3. Instructions for me to run your model on a separate data set

What should be in the report?

1. List of any assumptions that you made
2. Description of your methodology and solution path
3. List of algorithms and techniques you used
4. List of tools and frameworks you used
5. Results and evaluation of your models

How to evaluate the model

1. Use the Accuracy score

The restaurant test data for Subtask 1 Phase A evaluation of the SemEval 2016 Task 5: Aspect Based Sentiment Analysis (ABSA) for French (120 reviews, 668 sentences).

The Consumer Ratings & Reviews Software market is experiencing robust growth, projected to reach $478 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 12.7% from 2025 to 2033. This expansion is fueled by the increasing reliance of businesses across diverse sectors—including retail, logistics, media & entertainment, travel & hospitality, and healthcare—on online reviews to enhance brand reputation, drive customer loyalty, and inform business strategies. The shift towards cloud-based solutions simplifies deployment and accessibility, contributing significantly to market growth. Furthermore, the rising adoption of social media and the increasing consumer expectation for transparent and readily available reviews are key drivers. Competitive pressures are driving innovation, with companies constantly refining their offerings to provide comprehensive analytics, sentiment analysis, and automated response features. Segmentation by application and deployment type reflects the market's adaptability to diverse business needs. The North American market currently holds a significant share, driven by early adoption and established e-commerce infrastructure, but growth in regions like Asia-Pacific, fueled by rapid digitalization, presents lucrative opportunities. While the market enjoys considerable momentum, challenges remain. Data security and privacy concerns surrounding sensitive customer information are crucial considerations for businesses. Integration with existing CRM and marketing platforms can also pose complexities for some companies. However, the overall trend points towards continued expansion, with a focus on improving the accuracy and authenticity of reviews and the development of more sophisticated analytics capabilities to leverage review data for strategic decision-making. This includes incorporating AI and machine learning to better understand customer sentiment and identify areas for improvement. The emergence of specialized solutions catering to specific industry needs will further fragment the market, yet simultaneously enhance its reach and overall impact on business operations.

The REST dataset is derived from restaurant reviews, also containing review sentences and aspect sentiment annotations for aspect-based sentiment analysis.

Turkish Sentiment Dataset (Restaurant Reviews)

This dataset contains 15 manually created Turkish sentences labeled for sentiment analysis. The sentences are related to restaurant experiences and are categorized as positive, negative, or neutral.

  Labels

olumlu (positive) olumsuz (negative) nötr (neutral)

  Format

The dataset uses $ as a delimiter to avoid issues with commas inside the text.

  Example

Yemeklerin lezzeti ve sunumu tam bir şölendi, şefi… See the full description on the dataset page: https://huggingface.co/datasets/hanerdem/turkish-sentiment-dataset.

There are many contexts where dyadic data are present. In social networks, users are linked to a variety of items, defining interactions. In the social platform of TripAdvisor, users are linked to restaurants by means of reviews posted by them. Using the information of these interactions, we can get valuable insights for forecasting, proposing tasks related to recommender systems, sentiment analysis, text-based personalisation or text summarisation, among others. Furthermore, in the context of TripAdvisor there is a scarcity of public datasets and lack of well-known benchmarks for model assessment. We present six new TripAdvisor datasets from the restaurants of six different cities: London, New York, New Delhi, Paris, Barcelona and Madrid. If you use this data, please cite the following paper under submission process (preprint - arXiv) We exclusively collected the reviews written in English from the restaurants of each city. The tabular data is comprised of a set of six different CSV files, containing numerical, categorical and text features: parse_count: numerical (integer), corresponding number of extracted review by the web scraper (auto-incremental) author_id: categorical (string), univocal, incremental and anonymous identifier of the user (UID_XXXXXXXXXX) restaurant_name: categorical (string), name of the restaurant matching the review rating_review: numerical (integer), review score in the range 1-5 sample: categorical (string), indicating “positive” sample for scores 4-5 and “negative” for scores 1-3 review_id: categorical (string), univocal and internal identifier of the review (review_XXXXXXXXX) title_review: text, review title review_preview: text, preview of the review, truncated in the website when the text is very long review_full: text, complete review date: timestamp, publication date of the review in the format (day, month, year) city: categorical (string), city of the restaurant which the review was written for url_restaurant: text, restaurant url

Accuracies for Yelp restaurant dataset with 100.000 reviews.

In recent years, the surge in reviews and comments on newspapers and social media has made sentiment analysis a focal point of interest for researchers. Sentiment analysis is also gaining popularity in the Bengali language. However, Aspect-Based Sentiment Analysis is considered a difficult task in the Bengali language due to the shortage of perfectly labeled datasets and the complex variations in the Bengali language. This study used two open-source benchmark datasets of the Bengali language, Cricket, and Restaurant, for our Aspect-Based Sentiment Analysis task. The original work was based on the Random Forest, Support Vector Machine, K-Nearest Neighbors, and Convolutional Neural Network models. In this work, we used the Bidirectional Encoder Representations from Transformers, the Robustly Optimized BERT Approach, and our proposed hybrid transformative Random Forest and Bidirectional Encoder Representations from Transformers (tRF-BERT) models to compare the results with the existing work. After comparing the results, we can clearly see that all the models used in our work achieved better results than any of the previous works on the same dataset. Amongst them, our proposed transformative Random Forest and Bidirectional Encoder Representations from Transformers achieved the highest F1 score and accuracy. The accuracy and F1 score of aspect detection for the Cricket dataset were 0.89 and 0.85, respectively, and for the Restaurant dataset were 0.92 and 0.89 respectively.

OTA Review Analytics for Restaurants Market Outlook

According to our latest research, the OTA Review Analytics for Restaurants market size reached USD 1.25 billion globally in 2024, driven by the growing emphasis on digital reputation and customer feedback management. The market is expanding at a robust CAGR of 13.2% and is projected to reach USD 3.13 billion by 2033. This growth is primarily fueled by the increasing adoption of advanced analytics platforms by restaurants aiming to leverage online travel agency (OTA) review data for strategic decision-making and enhanced customer engagement.

The proliferation of digital platforms has transformed how restaurants interact with their customers, making OTA review analytics an essential tool for business intelligence. The surge in online reviews on platforms such as TripAdvisor, Yelp, and Google Reviews has compelled restaurants to invest in sophisticated analytics solutions. These tools enable them to monitor, analyze, and respond to customer feedback in real time, providing actionable insights that can directly impact service quality and revenue. As digital literacy among consumers rises, restaurants are under increasing pressure to maintain a positive online reputation, which in turn is driving the adoption of OTA review analytics solutions across the globe.

Another key growth driver is the integration of artificial intelligence and machine learning in OTA review analytics platforms. These technologies enable advanced sentiment analysis, trend detection, and predictive modeling, allowing restaurants to anticipate customer preferences and market shifts. The ability to benchmark performance against competitors and rapidly address negative feedback has become a critical differentiator, especially in highly competitive urban markets. Moreover, the scalability and flexibility offered by cloud-based deployment models have further accelerated market penetration, making these solutions accessible to both large restaurant chains and independent outlets.

The evolving regulatory landscape concerning data privacy and consumer rights is also shaping the OTA review analytics market. Restaurants are increasingly required to ensure transparency and compliance in how they collect, store, and utilize customer data. This has led to a surge in demand for analytics platforms that not only deliver robust insights but also adhere to stringent data protection standards. Additionally, the ongoing digital transformation in the hospitality sector, coupled with the rise of contactless dining experiences post-pandemic, has reinforced the need for real-time customer feedback analysis, further bolstering market growth.

From a regional perspective, North America continues to dominate the OTA Review Analytics for Restaurants market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high penetration of digital platforms, coupled with a mature restaurant industry, has driven early adoption in these regions. Emerging markets in Asia Pacific and Latin America are witnessing rapid growth, fueled by increasing internet penetration, rising disposable incomes, and a burgeoning middle class with a preference for digital-first dining experiences. As global travel and tourism rebound, the importance of OTA review analytics in shaping restaurant reputations and attracting international customers is expected to rise significantly.

Component Analysis

The component segment of the OTA Review Analytics for Restaurants market is bifurcated into software and services, each playing a pivotal role in the overall ecosystem. Software solutions form the backbone of review analytics, providing restaurants with the tools needed to aggregate, process, and interpret large volumes of OTA review data. These platforms often come equipped with dashboards, real-time alerts, and customizable reporting features, enabling restaurant managers to swiftly identify emerging trends and addr

📌 Kaggle Dataset Description: Data-Driven Insights for the Food Industry 🍽️📊 🚀 Explore the world of restaurant analytics with this rich dataset! This dataset contains 9,551 restaurant entries from various cities worldwide, providing insights into restaurant ratings, cuisines, pricing, customer votes, and more. Perfect for data analysis, machine learning, sentiment analysis, and business intelligence applications.

📝 Dataset Features ✔ Restaurant Details – ID, name, address, and locality ✔ Geographical Information – City, country, latitude, and longitude ✔ Cuisines Offered – Multicuisine options for each restaurant ✔ Cost Information – Average cost for two people, currency type, price range ✔ Online & Offline Services – Table booking, online delivery status ✔ Customer Ratings – Aggregate rating, rating color, and text reviews ✔ Customer Engagement – Number of votes for each restaurant

🔍 Use Cases 📊 Data Visualization – Identify trends in restaurant popularity and customer preferences 📉 Predictive Analytics – Forecast restaurant ratings based on historical data 🛒 Business Strategy – Insights for marketing, customer retention, and expansion strategies 📍 Geospatial Analysis – Explore restaurant density in different locations 🗣️ Sentiment Analysis – Extract sentiment from rating texts

📂 Dataset Access 💻 GitHub Repository: 🔗 https://github.com/Parthadee/Food-Industry-Analyze.git

🔥 Perfect for data enthusiasts, analysts, and ML practitioners! Start your restaurant analytics journey today!

Reputation Monitoring for Restaurants Market Outlook

According to our latest research, the global Reputation Monitoring for Restaurants market size reached USD 1.38 billion in 2024, exhibiting a robust growth trajectory. The market is projected to expand at a CAGR of 13.2% during the forecast period, reaching an estimated value of USD 4.09 billion by 2033. The surging adoption of digital platforms for restaurant reviews, heightened customer expectations, and the growing influence of online reputation on consumer decision-making are the primary growth drivers shaping the industry landscape.

One of the most significant growth factors for the Reputation Monitoring for Restaurants market is the escalating importance of digital presence in the food service industry. As consumers increasingly rely on online platforms for restaurant discovery and decision-making, the impact of customer feedback, ratings, and reviews on a restaurant’s reputation has never been more profound. Restaurants are now compelled to actively monitor, manage, and respond to online feedback to maintain a positive public image, attract new customers, and retain existing ones. The proliferation of review sites, social media channels, and third-party delivery platforms has created a complex ecosystem, making reputation monitoring solutions indispensable for restaurant operators seeking to stay ahead in a competitive market.

Another key driver propelling market growth is the integration of advanced technologies such as artificial intelligence, machine learning, and natural language processing into reputation monitoring solutions. These innovations enable restaurants to automate the collection, analysis, and interpretation of vast volumes of customer feedback from multiple sources. By leveraging AI-powered analytics, restaurants can gain actionable insights into customer sentiment, identify emerging trends, and proactively address potential issues before they escalate. This technological evolution not only enhances operational efficiency but also empowers restaurants to deliver personalized experiences, ultimately fostering customer loyalty and long-term business success.

Moreover, the increasing focus on brand management and competitive differentiation is fueling the demand for comprehensive reputation monitoring tools among restaurant chains and independent operators alike. In today’s hyper-connected world, a single negative review or social media post can significantly impact a restaurant’s brand perception and bottom line. As a result, businesses are investing in sophisticated reputation management platforms that offer real-time alerts, sentiment analysis, and benchmarking capabilities. These tools enable restaurants to swiftly respond to customer concerns, benchmark their performance against competitors, and implement data-driven strategies to enhance their overall brand reputation.

From a regional perspective, North America currently dominates the Reputation Monitoring for Restaurants market, accounting for the largest revenue share in 2024. The region’s leadership can be attributed to the high penetration of digital technologies, widespread use of online review platforms, and the presence of a large number of restaurant chains. Europe and Asia Pacific are also witnessing substantial growth, driven by rapid urbanization, increasing internet penetration, and changing consumer behaviors. Emerging markets in Latin America and the Middle East & Africa are expected to experience accelerated growth during the forecast period, as restaurants in these regions recognize the strategic importance of reputation management in driving customer acquisition and retention.

Component Analysis

The Reputation Monitoring for Restaurants market is segmented by component into software and services, each playing a pivotal role in shaping the industry’s evolution. Software solutions form the backbone of reputation monitoring, providing restaurants with automated tools to collect, analyze, and manage custome