Dataset: Sentiment Analysis for Social Media Posts in Arabic Dialect

Overview

This dataset contains a labeled collection of approximately 50,000 social media posts in various Arabic dialects. Each post has been manually annotated with sentiment labels, providing a rich resource for natural language processing and sentiment analysis research.

Dataset Owner

UM6P College of Computing

Content

• Posts: The dataset includes raw text data of social media posts written in different Arabic dialects.
• Sentiment Labels: Each post is labeled with one of the following sentiment categories:
  • Positive
  • Negative
  • Neutral

Features

• Post ID: A unique identifier for each social media post.
• Text: The content of the social media post in Arabic.
• Sentiment: The sentiment label assigned to the post (Positive, Negative, Neutral).

Format

The dataset is provided in a CSV format with the following columns: - Post_ID: Integer - Text: String - Sentiment: String (Positive, Negative, Neutral)

Usage

This dataset is ideal for tasks such as: - Training sentiment analysis models - Studying sentiment trends in Arabic social media - Exploring the linguistic characteristics of Arabic dialects - Benchmarking sentiment analysis tools

Example Data

Licensing

Please refer to the dataset license included in the dataset files for information on usage rights and restrictions.

Citation

An open access NLP dataset for Arabic dialects: data collection, labeling, and model construction, Elmehdi Boujou, Hamza Chataoui, Abdellah El Mekki, Saad Benjelloun, Ikram Chairi and Ismail Berrada MENACIS 2020 conference, In press.

Gain valuable insights with our comprehensive Social Media Dataset, designed to help businesses, marketers, and analysts track trends, monitor engagement, and optimize strategies. This dataset provides structured and reliable social media data from multiple platforms.

Dataset Features

User Profiles: Access public social media profiles, including usernames, bios, follower counts, engagement metrics, and more. Ideal for audience analysis, influencer marketing, and competitive research. Posts & Content: Extract posts, captions, hashtags, media (images/videos), timestamps, and engagement metrics such as likes, shares, and comments. Useful for trend analysis, sentiment tracking, and content strategy optimization. Comments & Interactions: Analyze user interactions, including replies, mentions, and discussions. This data helps brands understand audience sentiment and engagement patterns. Hashtag & Trend Tracking: Monitor trending hashtags, topics, and viral content across platforms to stay ahead of industry trends and consumer interests.

Customizable Subsets for Specific Needs Our Social Media Dataset is fully customizable, allowing you to filter data based on platform, region, keywords, engagement levels, or specific user profiles. Whether you need a broad dataset for market research or a focused subset for brand monitoring, we tailor the dataset to your needs.

Popular Use Cases

Brand Monitoring & Reputation Management: Track brand mentions, customer feedback, and sentiment analysis to manage online reputation effectively. Influencer Marketing & Audience Analysis: Identify key influencers, analyze engagement metrics, and optimize influencer partnerships. Competitive Intelligence: Monitor competitor activity, content performance, and audience engagement to refine marketing strategies. Market Research & Consumer Insights: Analyze social media trends, customer preferences, and emerging topics to inform business decisions. AI & Predictive Analytics: Leverage structured social media data for AI-driven trend forecasting, sentiment analysis, and automated content recommendations.

Whether you're tracking brand sentiment, analyzing audience engagement, or monitoring industry trends, our Social Media Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

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Social Media Listening Market Size 2025-2029

The social media listening market size is forecast to increase by USD 4.87 billion at a CAGR of 8.9% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing usage of social media platforms worldwide. With over 4.3 billion users as of 2021, social media has become a powerful tool for businesses to engage with their customers and gain valuable insights into consumer behavior and preferences. A key trend in this market is the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies in social media listening solutions, enabling more accurate and efficient data analysis. However, this market is not without challenges. Data privacy and regulatory compliance are becoming increasingly important, with stricter regulations being implemented to protect user data.
Companies must ensure they have strong data security measures in place to comply with these regulations and maintain consumer trust. Additionally, the vast amount of data generated on social media requires sophisticated analytics tools to extract meaningful insights. As such, businesses seeking to capitalize on the opportunities presented by the market must invest in advanced analytics solutions and prioritize data security and privacy. By doing so, they can effectively navigate the challenges and stay ahead of the competition.

What will be the Size of the Social Media Listening Market during the forecast period?

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Social media listening has emerged as a crucial business tool, enabling organizations to gain valuable insights from the vast amount of data generated through social media activity. This data is analyzed using techniques such as topic modeling and sentiment scoring to understand consumer behavior, preferences, and trends. Social media geographics and demographics provide essential context, while social media reach and volume measure the scope and impact of conversations. Social media pulse and sentiment reflect the current sentiment and buzz surrounding specific topics, offering real-time insights into market dynamics and trends.
Social media listening software is a vital component of the global market for social media analytics. Social media influence is assessed through the size and engagement of an audience, providing valuable information for marketing and brand management strategies. The social media landscape and heatmap offer a comprehensive view of the social media ecosystem, helping businesses stay informed and adapt to evolving patterns.

How is this Social Media Listening Industry segmented?

The social media listening industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Software
  Services


End-user

  Retail and e-commerce
  IT and telecom
  BFSI
  Media and entertainment
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa



  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The software segment is estimated to witness significant growth during the forecast period. This segment encompasses platforms and tools that offer real-time, automated, and scalable capabilities to monitor and analyze social media conversations across various channels such as Twitter, Facebook, Instagram, LinkedIn, TikTok, and Reddit. Real-time monitoring is a key feature of these solutions, empowering brands to identify mentions, trends, and sentiment as they emerge. By staying abreast of evolving topics, businesses can respond promptly to customer concerns, capitalize on viral events, and maintain a strong online presence. Artificial Intelligence (AI) and Machine Learning (ML) technologies are integral to social media listening software, enabling advanced topic identification, sentiment analysis, and trend recognition.

These technologies enable businesses to gain valuable customer insights, inform product development, and enhance customer experience. Social media listening platforms also offer data visualization and reporting features, allowing businesses to analyze and present their findings in a clear and actionable manner. Additionally, they provide social media dashboards, alerts, and governance tools to ensure compliance with social media policies and ethical standards. In summary, social media listening software plays a pivotal role in the global market for social media analytics, offering real-time insights and advanced capabilities to help businesses navigate the complex social media landscape and engage effectively with their audience.

Get a glance at the market report of share

Sentiment analysis of tech media articles using VADER package and co-occurrence analysis

Sources: Above 140k articles (01.2016-03.2019):

Gigaom 0.5%

Euractiv 0.9%

The Conversation 1.3%

Politico Europe 1.3%

IEEE Spectrum 1.8%

Techforge 4.3%

Fastcompany 4.5%

The Guardian (Tech) 9.2%

Arstechnica 10.0%

Reuters 11%

Gizmodo 17.5%

ZDNet 18.3%

The Register 19.5%

Methodology

The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers.

As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise.

The process included the following main steps:

The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology)

The articles containing the given social issue and co-occurring term are identified

The identified articles are divided into paragraphs

Social issue and co-occurring words are removed from the paragraph

The VADER sentiment analysis is carried out for every identified and modified paragraph

The average for the given word pair is calculated for the final result

Therefore, the procedure has been repeated for 100 words for all identified social issues.

The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down.

The presented tables include the most extreme co-occurring terms for the analysed social issue. The examples are chosen from the list of words with 30 most positive and 30 most negative sentiment. The presented graphs show the evolution of sentiments for social issues. The analysed paragraphs are selected the following way:

The articles containing the given social issue are identified

The paragraphs containing the social issue are selected for sentiment analysis

*Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.

Files

sentiments_mod11.csv sentiment score based on chosen unigrams

sentiments_mod22.csv sentiment score based on chosen bigrams

sentiments_cooc_mod11.csv, sentiments_cooc_mod12.csv, sentiments_cooc_mod21.csv, sentiments_cooc_mod22.csv combinations of co-occurrences: unigrams-unigrams, unigrams-bigrams, bigrams-unigrams, bigrams-bigrams

Humans spend most of their time in settlements, and the built environment of settlements may affect the residents’ sentiments. Research in this field is interdisciplinary, integrating urban planning and public health. However, it has been limited by the difficulty of quantifying subjective sentiments and the small sample size. This study uses 147,613 Weibo text check-ins in Xiamen from 2017 to quantify residents' sentiments in 1,096 neighborhoods in the city. A multilevel regression model and gradient boosting decision tree (GBDT) model are used to investigate the multilevel and nonlinear effects of the built environment of neighborhoods and subdistricts on residents' sentiments. The results show the following: 1) The multilevel regression model indicates that at the neighborhood level, a high land value, low plot ratio, low population density, more security facilities, and neighborhoods close to water are more likely to improve the residents’ sentiments. At the subdistrict level, more green space and commercial land, less industry, higher building density and road density, and a smaller migrant population are more likely to promote positive sentiments. Approximately 19% of the total variance in the sentiments occurred among subdistricts. 2) The number of security facilities, the proportion of green space and commercial land, and the density of buildings and roads are linearly correlated with residents' sentiments. The land value and the number of security facilities are basic needs and exhibit nonlinear correlations with sentiments. The plot ratio, population density, and the proportions of industrial land and the migrant population are advanced needs and are nonlinearly correlated with sentiments. The quantitative analysis of sentiments enables setting a threshold of the influence of the built environment on residents' sentiments in neighborhoods and surrounding areas. Our results provide data support for urban planning and implementing targeted measures to improve the living environment of residents.

Social Media Analytics Market Outlook

According to our latest research, the global Social Media Analytics market size reached USD 8.1 billion in 2024, driven by rapid digital transformation and the increasing need for actionable insights from social media channels. The market is expected to grow at a robust CAGR of 23.2% from 2025 to 2033, with the forecasted market size projected to reach USD 61.3 billion by 2033. This significant growth is attributed to the rising adoption of advanced analytics tools, the proliferation of social media platforms, and the growing emphasis on customer engagement and brand management across industries.

A primary driver of the Social Media Analytics market is the exponential increase in social media usage worldwide. With billions of active users across platforms such as Facebook, Instagram, Twitter, LinkedIn, and TikTok, organizations are increasingly leveraging social media analytics to monitor brand sentiment, understand consumer behavior, and refine marketing strategies. The growing volume of user-generated content provides a vast data pool that, when analyzed, offers valuable insights into market trends, consumer preferences, and competitive positioning. This data-driven approach empowers businesses to make informed decisions, optimize campaigns, and enhance customer experiences, fueling the demand for sophisticated social media analytics solutions.

Another key growth factor is the advancement in artificial intelligence (AI) and machine learning (ML) technologies, which have significantly enhanced the capabilities of social media analytics platforms. These technologies enable real-time data processing, predictive analytics, and automated sentiment analysis, allowing organizations to gain deeper and more accurate insights at scale. The integration of AI and ML not only improves the efficiency of data analysis but also enables the identification of emerging trends, potential risks, and new opportunities. As a result, companies across various sectors are investing in AI-powered analytics tools to stay ahead in a highly competitive digital landscape.

The increasing importance of personalized marketing and customer-centric strategies is also propelling the growth of the Social Media Analytics market. Businesses are utilizing analytics to segment their audience, tailor content, and deliver targeted campaigns that resonate with specific customer groups. This not only enhances engagement and conversion rates but also fosters brand loyalty and long-term customer relationships. Furthermore, the rise of influencer marketing and the need to measure campaign effectiveness are prompting brands to adopt advanced analytics tools to track engagement metrics, ROI, and overall impact on brand reputation.

Regionally, North America continues to dominate the Social Media Analytics market, accounting for the largest share in 2024 due to the presence of major technology players, high digital adoption rates, and substantial investments in analytics infrastructure. However, the Asia Pacific region is witnessing the fastest growth, driven by the rapid expansion of the digital economy, increasing internet penetration, and the growing adoption of social media platforms among businesses and consumers. Europe also holds a significant market share, supported by stringent data privacy regulations and the rising demand for compliance-driven analytics solutions.

Component Analysis

The Social Media Analytics market by component is bifurcated into software and services, each playing a pivotal role in the ecosystem. The software segment encompasses a wide range of analytics tools and platforms designed to collect, process, and visualize data from various social media channels. These solutions are increasingly equipped with advanced features such as AI-driven sentiment analysis, real-time monitoring, and customizable dashboards, enabling organizations to derive actionable insights efficiently. As businesses continue to prioritize data-driven decision-making, the demand for comprehensive

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Description:

Twitter is an online Social Media Platform where people share their their though as tweets. It is observed that some people misuse it to tweet hateful content. Twitter is trying to tackle this problem and we shall help it by creating a strong NLP based-classifier model to distinguish the negative tweets & block such tweets. Can you build a strong classifier model to predict the same?

Each row contains the text of a tweet and a sentiment label. In the training set you are provided with a word or phrase drawn from the tweet (selected_text) that encapsulates the provided sentiment.

Make sure, when parsing the CSV, to remove the beginning / ending quotes from the text field, to ensure that you don't include them in your training.

You're attempting to predict the word or phrase from the tweet that exemplifies the provided sentiment. The word or phrase should include all characters within that span (i.e. including commas, spaces, etc.)

Columns:

1. textID - unique ID for each piece of text
2. text - the text of the tweet
3. sentiment - the general sentiment of the tweet

Acknowledgement:

The dataset is download from Kaggle Competetions:
https://www.kaggle.com/c/tweet-sentiment-extraction/data?select=train.csv

Objective:

• Understand the Dataset & cleanup (if required).
• Build classification models to predict the twitter sentiments.
• Compare the evaluation metrics of vaious classification algorithms.

Please cite the following paper when using this dataset:

N. Thakur, “Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis”, Proceedings of the 7th International Conference on Machine Learning and Natural Language Processing (MLNLP 2024), Chengdu, China, October 18-20, 2024 (Paper accepted for publication, Preprint available at: https://arxiv.org/abs/2410.03293)

Abstract

The outbreak of COVID-19 served as a catalyst for content creation and dissemination on social media platforms, as such platforms serve as virtual communities where people can connect and communicate with one another seamlessly. While there have been several works related to the mining and analysis of COVID-19-related posts on social media platforms such as Twitter (or X), YouTube, Facebook, and TikTok, there is still limited research that focuses on the public discourse on Instagram in this context. Furthermore, the prior works in this field have only focused on the development and analysis of datasets of Instagram posts published during the first few months of the outbreak. The work presented in this paper aims to address this research gap and presents a novel multilingual dataset of 500,153 Instagram posts about COVID-19 published between January 2020 and September 2024. This dataset contains Instagram posts in 161 different languages. After the development of this dataset, multilingual sentiment analysis was performed using VADER and twitter-xlm-roberta-base-sentiment. This process involved classifying each post as positive, negative, or neutral. The results of sentiment analysis are presented as a separate attribute in this dataset.

For each of these posts, the Post ID, Post Description, Date of publication, language code, full version of the language, and sentiment label are presented as separate attributes in the dataset.

The Instagram posts in this dataset are present in 161 different languages out of which the top 10 languages in terms of frequency are English (343041 posts), Spanish (30220 posts), Hindi (15832 posts), Portuguese (15779 posts), Indonesian (11491 posts), Tamil (9592 posts), Arabic (9416 posts), German (7822 posts), Italian (5162 posts), Turkish (4632 posts)

There are 535,021 distinct hashtags in this dataset with the top 10 hashtags in terms of frequency being #covid19 (169865 posts), #covid (132485 posts), #coronavirus (117518 posts), #covid_19 (104069 posts), #covidtesting (95095 posts), #coronavirusupdates (75439 posts), #corona (39416 posts), #healthcare (38975 posts), #staysafe (36740 posts), #coronavirusoutbreak (34567 posts)

The following is a description of the attributes present in this dataset

• Post ID: Unique ID of each Instagram post
• Post Description: Complete description of each post in the language in which it was originally published
• Date: Date of publication in MM/DD/YYYY format
• Language code: Language code (for example: “en”) that represents the language of the post as detected using the Google Translate API
• Full Language: Full form of the language (for example: “English”) that represents the language of the post as detected using the Google Translate API
• Sentiment: Results of sentiment analysis (using the preprocessed version of each post) where each post was classified as positive, negative, or neutral

Open Research Questions

This dataset is expected to be helpful for the investigation of the following research questions and even beyond:

1. How does sentiment toward COVID-19 vary across different languages?
2. How has public sentiment toward COVID-19 evolved from 2020 to the present?
3. How do cultural differences affect social media discourse about COVID-19 across various languages?
4. How has COVID-19 impacted mental health, as reflected in social media posts across different languages?
5. How effective were public health campaigns in shifting public sentiment in different languages?
6. What patterns of vaccine hesitancy or support are present in different languages?
7. How did geopolitical events influence public sentiment about COVID-19 in multilingual social media discourse?
8. What role does social media discourse play in shaping public behavior toward COVID-19 in different linguistic communities?
9. How does the sentiment of minority or underrepresented languages compare to that of major world languages regarding COVID-19?
10. What insights can be gained by comparing the sentiment of COVID-19 posts in widely spoken languages (e.g., English, Spanish) to those in less common languages?

All the Instagram posts that were collected during this data mining process to develop this dataset were publicly available on Instagram and did not require a user to log in to Instagram to view the same (at the time of writing this paper).

General: The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge and Workshop (MuSe) is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), and the sentiment analysis community (symbol-based).

We introduce the novel dataset MuSe-CAR that covers the range of aforementioned desiderata. MuSe-CAR is a large (>36h), multimodal dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity.

We have designed MuSe-CAR to be of high voice and video quality, as informative video social media content, as well as everyday recording devices have improved in recent years. This enables robust learning, even with a high degree of novel, in-the-wild characteristics, for example as related to: i) Video: Shot size (a mix of closeup, medium, and long shots), face-angle (side, eye, low, high), camera motion (free, free but stable, and free but unstable, switch, e.g., zoom, fixed), reviewer visibility (full body, half-body, face only, and hands only), highly varying backgrounds, and people interacting with objects (car parts). ii) Audio: Ambient noises (car noises, music), narrator and host diarisation, diverse microphone types, and speaker locations. iii) Text: Colloquialisms, and domain-specific terms.

Snapshot img

Social Media Analytics Market Size 2025-2029

The social media analytics market size is forecast to increase by USD 21.2 billion, at a CAGR of 35.2% between 2024 and 2029.

The market is experiencing significant growth, driven by the expanding availability and complexity of social media data. Businesses increasingly recognize the value of social media insights to inform marketing strategies, enhance customer engagement, and gauge brand reputation. In response, social media platforms continue to roll out advanced targeting options, enabling more precise audience segmentation and personalized messaging. However, the surging use of social media data also presents challenges. Interpreting unstructured data from various sources remains a formidable task, requiring sophisticated analytics tools and expertise.
Companies must navigate these complexities to effectively harness the power of social media analytics and stay competitive in today's digital landscape. To succeed, organizations need to invest in advanced analytics solutions, cultivate data literacy skills, and establish clear data governance policies. By addressing these challenges, businesses can unlock valuable insights from social media data and capitalize on emerging opportunities in this dynamic market.

What will be the Size of the Social Media Analytics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, offering valuable insights for businesses across various sectors. Hashtag tracking and sentiment classification help organizations understand public perception and engagement with their brand. Engagement metrics, share of voice, and trend analysis algorithms provide valuable data for brand reputation management and customer journey mapping. Social media ROI, influencer marketing metrics, and sentiment scoring offer insights into the effectiveness of advertising campaigns. User behavior patterns, predictive modeling, and anomaly detection enable businesses to anticipate trends and respond to crises in real-time. Social media listening, lead generation attribution, influencer identification, and customer satisfaction scores provide actionable insights for community management and crisis communication management.

Data visualization dashboards and social listening tools facilitate effective audience segmentation and conversational AI. Reach forecasting, content performance, keyword analysis, and campaign effectiveness metrics offer valuable insights for optimizing social media strategies. Platform-specific insights enable businesses to tailor their approach to each social media channel. According to recent market research, the market is expected to grow by over 15% annually, reflecting the increasing importance of social media data for businesses. For instance, a retail company used social media listening tools to monitor customer conversations and identified a trend in customer complaints about product packaging. The company responded by redesigning the packaging, resulting in a 12% increase in sales.

This example highlights the potential impact of social media analytics on business performance.

How is this Social Media Analytics Industry segmented?

The social media analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Retail
  Government
  Media and entertainment
  Travel
  Others


Application

  Sales and marketing management
  Customer experience management
  Competitive intelligence
  Risk management
  Public safety and law enforcement


Deployment

  On-premises
  Cloud


Type

  Predictive analytics
  Prescriptive analytics
  Descriptive analytics
  Diagnostics analytics


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By End-user Insights

The retail segment is estimated to witness significant growth during the forecast period.

Social media analytics plays a pivotal role in retail marketing, enabling businesses to track and analyze customer engagement, sentiment, and trends in real-time. Tools such as hashtag tracking, sentiment classification, and engagement metrics help retailers understand their audience's preferences and behavior patterns. Share of voice and trend analysis algorithms provide insights into market dynamics and brand reputation management. Customer journey mapping and social media ROI measurement allow businesses to optimize their marketing strategies and improve sales. Influencer marketing metrics, sentiment scoring, and advertising

As the influence and risk of infectious diseases increase, efforts are being made to predict the number of confirmed infectious disease patients, but research involving the qualitative opinions of social media users is scarce. However, social data can change the psychology and behaviors of crowds through information dissemination, which can affect the spread of infectious diseases. Existing studies have used the number of confirmed cases and spatial data to predict the number of confirmed cases of infectious diseases. However, studies using opinions from social data that affect changes in human behavior in relation to the spread of infectious diseases are inadequate. Therefore, herein, we propose a new approach for sentiment analysis of social data by using opinion mining and to predict the number of confirmed cases of infectious diseases by using machine learning techniques. To build a sentiment dictionary specialized for predicting infectious diseases, we used Word2Vec to expand the existing sentiment dictionary and calculate the daily sentiment polarity by dividing it into positive and negative polarities from collected social data. Thereafter, we developed an algorithm to predict the number of confirmed infectious patients by using both positive and negative polarities with DNN, LSTM and GRU. The method proposed herein showed that the prediction results of the number of confirmed cases obtained using opinion mining were 1.12% and 3% better than those obtained without using opinion mining in LSTM and GRU model, and it is expected that social data will be used from a qualitative perspective for predicting the number of confirmed cases of infectious diseases.

MuSe-Wild of MuSe2020: Predicting the level of emotional dimensions (arousal, valence) in a time-continuous manner from audio-visual recordings. This package includes only MuSe-Wild features (all partitions) and annotations of the training and development set (test scoring via the MuSe website).

General: The purpose of the Multimodal Sentiment Analysis in Real-life media Challenge and Workshop (MuSe) is to bring together communities from different disciplines; mainly, the audio-visual emotion recognition community (signal-based), and the sentiment analysis community (symbol-based).

We introduce the novel dataset MuSe-CAR that covers the range of aforementioned desiderata. MuSe-CAR is a large (>36h), multimodal dataset which has been gathered in-the-wild with the intention of further understanding Multimodal Sentiment Analysis in-the-wild, e.g., the emotional engagement that takes place during product reviews (i.e., automobile reviews) where a sentiment is linked to a topic or entity.

We have designed MuSe-CAR to be of high voice and video quality, as informative video social media content, as well as everyday recording devices have improved in recent years. This enables robust learning, even with a high degree of novel, in-the-wild characteristics, for example as related to: i) Video: Shot size (a mix of close-up, medium, and long shots), face-angle (side, eye, low, high), camera motion (free, free but stable, and free but unstable, switch, e.g., zoom, fixed), reviewer visibility (full body, half-body, face only, and hands only), highly varying backgrounds, and people interacting with objects (car parts). ii) Audio: Ambient noises (car noises, music), narrator and host diarisation, diverse microphone types, and speaker locations. iii) Text: Colloquialisms, and domain-specific terms.

Snapshot img

Artificial Intelligence in Social Media Market Size 2024-2028

The artificial intelligence (AI) in social media market size is forecast to increase by USD 5.57 billion at a CAGR of 27.82% between 2023 and 2028.

Artificial Intelligence is revolutionizing the social media market by enabling advanced data analysis and personalized user experiences. The growing demand for data integration and visual analytics is a significant market growth factor, as businesses seek to gain insights from vast amounts of social media data.
Additionally, the increasing use of social media for advertising has created a need for AI-powered solutions to effectively target and engage consumers. However, the lack of a skilled workforce for the development of AI algorithms poses a challenge for market growth. Despite this, the potential benefits of AI in social media, including improved customer engagement and enhanced marketing capabilities, are driving innovation and investment in this area.

Artificial Intelligence in Social Media Market Analysis

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How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD Billion' for the period 2024-2028, as well as historical data from 2018 - 2022 for the following segments.

Application

  Predictive risk management
  Consumer experience management
  Sales and marketing


End-user

  Large Enterprise
  SMEs


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    India


  Middle East and Africa



  South America

By Application Insights

The predictive risk management segment is estimated to witness significant growth during the forecast period. Artificial Intelligence (AI) is revolutionizing the social media market, particularly in areas of advertising, data security, and user experience. Machine learning programs are used for content recommendation, fraud detection, and predictive risk assessment, enabling large enterprises to optimize their sales and marketing efforts and enhance customer experience management. AI technology is also employed for content creation, curation, and personalization, catering to user behavior, preferences, and sentiments. Sentiment analysis, chatbots, and automated moderation are essential tools for governments and businesses to ensure the ethical use of consumer data for targeted advertising campaigns. AI-enabled smartphones and Real-Time Operating Systems provide real-time information, daily news, and live updates, enhancing user satisfaction and engagement.

Furthermore, AI experts anticipate the growing role of virtual assistants, deep learning, and predictive modeling in the advertising industry, further transforming the social media sector.

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The predictive risk management segment was valued at USD 290.00 million in 2018 and showed a gradual increase during the forecast period.

Will Social Media landscape make North America the largest contributor to the Artificial Intelligence (AI) in Social Media Market?-

North America is estimated to contribute 41% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions Request Free Sample

The social media landscape in North America is witnessing significant growth due to the increasing adoption of advanced technologies such as cognitive computing, image recognition, and artificial intelligence (AI) by various industries, including retail, manufacturing, and healthcare. The region's high internet penetration and the millennial generation's preference for social media networking make it an attractive market for brands that are conscious about their image and customer demographics. Advanced analytics derived from unstructured data, metadata, comments, vlogs, podcasts, video sharing sites, and photo sharing sites are crucial for marketing campaigns and public reviews. Telecom organizations are leveraging LongTerm Evolution (LTE) and AdvancedLTE to enhance their social media presence and engage with customers effectively. System failure and security concerns have led to the increased use of AI technologies for social listening and customer engagement. The growth of the market is further fueled by global conferences, product launches, and product exhibitions, where organizations use AI to host and promote events.

Our researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

Market Dynamics

Artificial I

AI in Social Media Market Outlook

According to our latest research, the AI in Social Media market size reached USD 2.45 billion globally in 2024, reflecting robust adoption across diverse industries. The market is projected to grow at a CAGR of 27.8% from 2025 to 2033, reaching an estimated USD 22.5 billion by the end of the forecast period. This significant growth is being driven by the increasing integration of artificial intelligence to enhance user engagement, automate content creation, and deliver personalized experiences on social media platforms. As per our latest research, the proliferation of AI-powered tools and analytics solutions is fundamentally transforming the way organizations leverage social media for marketing, customer service, and brand management.

One of the primary growth factors for the AI in Social Media market is the surging demand for intelligent customer engagement solutions. AI-driven chatbots, virtual assistants, and automated response systems are enabling brands to interact with customers in real time, providing instant support and personalized recommendations. This not only enhances user satisfaction but also streamlines business processes, allowing organizations to handle higher volumes of customer queries with minimal human intervention. The ability of AI to analyze vast amounts of unstructured social data and extract actionable insights is further fueling adoption, as businesses seek to understand customer sentiment, emerging trends, and market dynamics more effectively.

Another key driver is the exponential growth in content creation and curation powered by AI algorithms. Social media platforms are increasingly leveraging machine learning and natural language processing to generate, filter, and recommend content that resonates with individual users. AI-based tools can automatically produce text, images, and videos tailored to specific audiences, enabling marketers and influencers to maintain a consistent and engaging online presence. The efficiency and scalability offered by AI in managing content workflows are particularly valuable in the era of information overload, where capturing user attention is increasingly challenging. This trend is expected to accelerate as generative AI models become more sophisticated, supporting a wider range of creative applications across social networks.

The growing emphasis on social media monitoring and advanced analytics also contributes to the expansion of the AI in Social Media market. Businesses are investing in AI-powered solutions to track brand mentions, monitor competitor activities, and detect potential crises in real time. These systems leverage sentiment analysis, image recognition, and predictive analytics to provide actionable intelligence, enabling organizations to make data-driven decisions and respond proactively to market shifts. The integration of AI with advertising platforms further enhances targeting capabilities, optimizing campaign performance and maximizing return on investment. As regulatory scrutiny around data privacy intensifies, AI technologies are also being harnessed to ensure compliance and safeguard user information, adding another layer of value for enterprises.

Regionally, North America continues to dominate the AI in Social Media market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The presence of leading technology companies, early adoption of advanced digital solutions, and a mature social media ecosystem underpin North America's leadership. However, Asia Pacific is emerging as a high-growth region, driven by rapid digitalization, expanding internet penetration, and the proliferation of mobile-first social platforms. Latin America and the Middle East & Africa are also witnessing steady growth as businesses in these regions increasingly recognize the strategic importance of AI in enhancing social media engagement and brand visibility.

Component Analysis

The component segment of the AI in Social Medi

Context

Data depicted are messages ('tweets' from here on) live-streamed on Twitter.com from 30 minutes prior to 30 minutes after Game 7 of the Stanley Cup between the Toronto Maple Leafs, and the Boston Bruins. Nearly 3000 spatially referenced tweets are represented here (of an estimated 80-100 thousand (400+MB)), between two files. Tweet content was then run through the VADER sentiment analyzer package, and a "sentiment composite score" injected into each tweet. This dataset is primarily to serve as an introduction to spatialized sentiment analysis, enjoy!

Keywords utilized: bruins leafs #game7 #StanleyCup #goleafsgo #NHLBruins #leafsforever #Bruins

Uses:

Sentiment analyzer comparisons: because the out-of-the-box options for VADER are utilized, comparisons to other sentiment analyzers can be made (example: textblob).

Spatial and Temporal sentiment analysis: comparisons of sentiment based on location, tweet keyword, time, time vs. game events, can be made.

Content

The "as_point_stanleycup07.geojson" file contains tweets with geojson point geometry based off of the users' device GPS. Each tweet injected with a "sentiment composite score" analyzed with VADER sentiment analyzer.

The "as_place_stanleycup07.geojson" file contains tweets with geojson polygon geometry (rectangles) based off of a "known area" such as a city, or state. You may want to reduce this geometry to their centroids for spatial analysis purposes. Each tweet injected with a "sentiment composite score" analyzed with VADER sentiment analyzer.

Acknowledgements

Libraries utilized, tweepy, vader, (not used, but also recommended: twarc)

Inspiration

Sentiment analysis of tech media articles using VADER package and co-occurrence analysis

Sources with weights:

• Euractiv 5%
• The Conversation 5%
• Politico Europe 5 %
• IEEE Spectrum 5 %
• Techforge 5%
• Fastcompany 5%
• The Guardian (Tech) 12%
• Arstechnica 5%
• Reuters 5%
• Gizmodo 9%
• ZDNet 9%
• The Register 12%
• The Verge 9%
• TechCrunch 9%

Methodology

The sentiment analysis has been prepared using VADER*, an open-source lexicon and rule-based sentiment analysis tool. VADER is specifically designed for social media analysis, but can be also applied for other text sources. The sentiment lexicon was compiled using various sources (other sentiment data sets, Twitter etc.) and was validated by human input. The advantage of VADER is that the rule-based engine includes word-order sensitive relations and degree modifiers.

As VADER is more robust in the case of shorter social media texts, the analysed articles have been divided into paragraphs. The analysis have been carried out for the social issues presented in the co-occurrence exercise.

The process included the following main steps:

• The 100 most frequently co-occurring terms are identified for every social issue (using the co-occurrence methodology)
• The articles containing the given social issue and co-occurring term are identified
• The identified articles are divided into paragraphs
• Social issue and co-occurring words are removed from the paragraph
• The VADER sentiment analysis is carried out for every identified and modified paragraph
• The average for the given word pair is calculated for the final result

Therefore, the procedure has been repeated for 100 words for all identified social issues.

The sentiment analysis resulted in a compound score for every paragraph. The score is calculated from the sum of the valence scores of each word in the paragraph, and normalised between the values -1 (most extreme negative) and +1 (most extreme positive). Finally, the average is calculated from the paragraph results. Removal of terms is meant to exclude sentiment of the co-occurring word itself, because the word may be misleading, e.g. when some technologies or companies attempt to solve a negative issue. The neighbourhood's scores would be positive, but the negative term would bring the paragraph's score down.

The presented tables include the most extreme co-occurring terms for the analysed social issue. The examples are chosen from the list of words with 30 most positive and 30 most negative sentiment. The presented graphs show the evolution of sentiments for social issues. The analysed paragraphs are selected the following way:

• The articles containing the given social issue are identified
• The paragraphs containing the social issue are selected for sentiment analysis

*Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.

As the influence and risk of infectious diseases increase, efforts are being made to predict the number of confirmed infectious disease patients, but research involving the qualitative opinions of social media users is scarce. However, social data can change the psychology and behaviors of crowds through information dissemination, which can affect the spread of infectious diseases. Existing studies have used the number of confirmed cases and spatial data to predict the number of confirmed cases of infectious diseases. However, studies using opinions from social data that affect changes in human behavior in relation to the spread of infectious diseases are inadequate. Therefore, herein, we propose a new approach for sentiment analysis of social data by using opinion mining and to predict the number of confirmed cases of infectious diseases by using machine learning techniques. To build a sentiment dictionary specialized for predicting infectious diseases, we used Word2Vec to expand the existing sentiment dictionary and calculate the daily sentiment polarity by dividing it into positive and negative polarities from collected social data. Thereafter, we developed an algorithm to predict the number of confirmed infectious patients by using both positive and negative polarities with DNN, LSTM and GRU. The method proposed herein showed that the prediction results of the number of confirmed cases obtained using opinion mining were 1.12% and 3% better than those obtained without using opinion mining in LSTM and GRU model, and it is expected that social data will be used from a qualitative perspective for predicting the number of confirmed cases of infectious diseases.

Stockpulse’s Emotional Data Intelligence is the next level of comprehending and processing social media data, powered by deep learning and artificial intelligence. Able to understand the emotions expressed by market participants, investors improve their asset allocation and enhance seeking Alpha. With the expertise of more than ten years in data science and machine learning, we provide first-class solutions for customized data delivery. Stockpulse's Natural Language Processing traces Social Media squawk in English, German, and Mandarin. We collect our data from several thousand different sources worldwide, such as news articles & journals, blogs, social media, and forums. Stockpulse's Artificial Intelligence processes all of these unstructured data sets and turns them into viable and comprehensible information. Data intelligence is the understanding and structuring of large amounts of data called data mining. This user-generated content comprises a substantial part of communication in Social Media. We identify this emotionally expressed content and classify it as “Emotional Data.” Both parts of Social Media analysis are unstructured and must be processed to create value for financial investors. Stockpulse’s artificial intelligence filters this bulk of information, selects, structures and converts it into intelligible data for the financial industry. The outcome is Emotional Data Intelligence! Did you know: Stockpulse's outstanding coverage goes beyond equities, indices, and commodities to other asset classes such as FX and cryptocurrencies.

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Social Networking Market Size 2025-2029

The social networking market size is forecast to increase by USD 312.3 billion, at a CAGR of 21.6% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing internet penetration worldwide. This expansion is fueled by the rising number of active social media users, enabling businesses to reach a larger audience through digital platforms. However, the market's growth is not without challenges. Privacy concerns are increasingly obstructing market expansion, as users become more conscious of their online data and demand greater control over their information. Social media advertisements, a major revenue source for social networking companies, are gaining traction, creating intense competition among market players. Companies must navigate these challenges by addressing privacy concerns through transparent data handling policies and effective user data protection measures.
Additionally, innovation in advertising formats and targeting strategies will be crucial for businesses to differentiate themselves and maintain a competitive edge. In summary, the market presents both opportunities and challenges, with increasing internet penetration driving growth while privacy concerns and intense competition shaping the strategic landscape. Companies must effectively address these challenges to capitalize on the market's potential and stay ahead of the competition.

What will be the Size of the Social Networking Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic patterns emerging across various sectors. Customer acquisition and sales conversion are key areas of focus, as social CRM and mobile marketing strategies gain traction. User engagement remains a priority, with social listening and social network analysis providing valuable insights. Big data and data analytics play a crucial role in informing business decisions, while media relations and crisis communication strategies adapt to the digital landscape. Influencer marketing and viral marketing campaigns continue to shape consumer behavior, with conversion optimization and organic reach driving growth. Live streaming and user-generated content offer new opportunities for brands to engage with audiences.

Data visualization and machine learning are transforming how businesses analyze and respond to market trends. E-commerce platforms and social commerce are disrupting traditional retail models, with advertising platforms and social media marketing becoming essential tools for businesses. Algorithm updates and link building strategies impact search engine optimization and content strategy. Privacy concerns and network externalities are shaping the platform economics, while network effects drive user growth. Content creation tools and search engine optimization are essential for effective brand building, with public relations and sentiment analysis playing a critical role in reputation management. Video marketing and customer satisfaction are key drivers of brand loyalty, with data security and competitor analysis essential for maintaining a competitive edge.Social media platforms continue to evolve, offering new opportunities for businesses to connect with their audiences and build strong brands.

How is this Social Networking Industry segmented?

The social networking industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Advertising
  In-app purchase
  Paid apps


Distribution Channel

  Google
  Apple
  App Store Distribution


Service

  Communication
  Entertainment
  Socialization
  Marketing
  Customer service


Platform

  Website-based
  Mobile apps
  Hybrid platforms


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Type Insights

The advertising segment is estimated to witness significant growth during the forecast period.

In the dynamic landscape of the market, various entities intertwine to shape its evolution. Big data and machine learning fuel social media analytics, enabling targeted advertising, conversion optimization, and customer satisfaction. Social listening and sentiment analysis inform brand monitoring, reputation management, and crisis communication. Social crm and community management foster customer loyalty and engagement. Mobile marketing, including user-generated content and live streaming, e

Snapshot img

Social Media Management Software Market Size 2025-2029

The social media management software market size is forecast to increase by USD 54.98 billion, at a CAGR of 24.9% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing number of users on social media platforms. With billions of active social media users worldwide, businesses are recognizing the need for efficient and effective social media management solutions. A key trend in this market is the integration of advanced analytics capabilities into social media management software. This enables businesses to gain valuable insights into consumer behavior, preferences, and trends, informing data-driven marketing strategies. However, the high price point of application software poses a challenge for smaller businesses and startups, limiting their access to these tools.
To capitalize on market opportunities, companies must focus on offering competitive pricing and flexible pricing models. Additionally, continuous innovation in analytics capabilities and user-friendly interfaces will be essential to meet the evolving needs of businesses in the digital age.

What will be the Size of the Social Media Management Software Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The social media management market continues to evolve, with dynamic trends shaping its landscape. Integration of social media APIs facilitates seamless data flow between platforms and other business systems. Social media crowdsourcing harnesses user-generated content for marketing initiatives, while gamification adds an engaging element to brand interactions. Social media optimization ensures content is tailored for each platform, enhancing reach and engagement. Auditing tools assess performance, identifying areas for improvement. Monitoring solutions track brand mentions and sentiment analysis provides valuable customer insights.

Social media strategy is crucial, balancing automation and human intervention for effective community management. Reporting and analytics offer ROI measurements, while advertising targets specific demographics. Contests and sweepstakes foster engagement, with compliance a key consideration. Partnerships and storytelling expand reach, aligning with emerging trends. Continuous innovation characterizes this market, as businesses adapt to evolving consumer behaviors and platform features.

How is this Social Media Management Software Industry segmented?

The social media management software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Retail and consumer goods
  Healthcare and life sciences
  IT and telecom
  Government and public sector
  Others


Deployment

  Cloud-based
  On-premises


Sector

  Large enterprises
  Small and medium sized enterprises


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By End-user Insights

The retail and consumer goods segment is estimated to witness significant growth during the forecast period.

Social media integration is a crucial aspect of modern business strategies, enabling seamless communication between various platforms and retail systems. Measuring social media performance through analytics and reporting tools is essential for understanding engagement levels, sentiment analysis, and reach. Social media management solutions facilitate scheduling, automation, and monitoring of multiple accounts, ensuring consistent brand messaging and community management. Social media platforms themselves offer various features for contests, giveaways, and sweepstakes to boost engagement and reach. Social media advertising provides targeted campaigns to reach specific audiences, while optimization and auditing tools help improve performance. Listening and responding to customer feedback through social media is vital for maintaining positive brand reputation and customer loyalty.

Social media marketing strategies focus on storytelling, partnerships, and trends to create immersive and harmonious brand experiences. Sentiment analysis and engagement metrics help brands understand their audience's preferences and tailor their messaging accordingly. Social media contests, gamification, and crowdsourcing foster a sense of community and encourage user-generated content. Social media optimization and compliance are essential for maintaining brand integrity and adhering to industry regulations. Social media trends continuously evolve, making it necessary for businesses to sta