Understand the mood of social media with sentiment analysis. Monitor brand mentions, analyze feedback, and tailor strategies.

Problem Statement 👉 Download the case studies here A global consumer goods company struggled to understand customer sentiment across various social media platforms. With millions of posts, reviews, and comments generated daily, manually tracking and analyzing public opinion was inefficient. The company needed an automated solution to monitor brand perception, address negative feedback promptly, and leverage insights for marketing strategies. Challenge Analyzing social media sentiment posed… See the full description on the dataset page: https://huggingface.co/datasets/globosetechnology12/Sentiment-Analysis-for-Social-Media-Monitoring.

This machine-generated dataset simulates social media engagement data across various metrics, including likes, shares, comments, impressions, sentiment scores, toxicity, and engagement growth. It is designed for analysis and visualization of trends, buzz frequency, public sentiment, and user behavior on digital platforms.

The dataset can be used to:

Identify spikes or drops in engagement

Analyze changes in sentiment over time

Build dashboards for digital trend tracking

Test algorithms for sentiment analysis or trend prediction

“Supplemental material, Original Twitter Dataset, for "Does Negative Social Media Sentiment Affect Stock Prices? Evidence from the Canadian Energy Sector" by Diego Corry and Hamish van der Ven in Business and Society.

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.

The social media analytics market is experiencing robust growth, driven by the escalating volume of social media data and the increasing need for businesses to understand and leverage this information for strategic decision-making. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $45 billion by 2033. Key drivers include the rising adoption of social listening tools for brand monitoring and customer feedback analysis, the growing sophistication of AI-powered analytics platforms offering predictive capabilities, and the increasing demand for real-time insights to inform marketing strategies and crisis management. Trends such as the integration of social media analytics with other data sources (e.g., CRM, web analytics) and the emergence of specialized solutions for specific industries (e.g., healthcare, finance) are further fueling market expansion. However, challenges such as data privacy concerns, the complexity of social media data, and the need for skilled professionals to interpret and utilize the insights effectively pose some restraints on market growth. The market is segmented by solution type (e.g., social listening, sentiment analysis, brand monitoring), deployment mode (cloud, on-premise), and industry vertical. Leading players like IBM, Oracle, Salesforce, and Adobe Systems are shaping the market landscape through continuous innovation and strategic acquisitions. The competitive landscape is characterized by a mix of established technology vendors and specialized social media analytics firms. While large players offer comprehensive platforms integrating social media analytics with other enterprise solutions, smaller specialized vendors focus on niche functionalities and specific industry needs. The North American region currently holds the largest market share, followed by Europe and Asia-Pacific. However, growth in emerging markets is expected to accelerate in the coming years, driven by increasing internet penetration and mobile adoption. The forecast period (2025-2033) is expected to witness significant technological advancements, potentially including the wider adoption of blockchain technology for secure data management and improved analytical capabilities based on advancements in natural language processing (NLP) and machine learning (ML).

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.

Our Twitter Sentiment Analysis Dataset provides a comprehensive collection of tweets, enabling businesses, researchers, and analysts to assess public sentiment, track trends, and monitor brand perception in real time. This dataset includes detailed metadata for each tweet, allowing for in-depth analysis of user engagement, sentiment trends, and social media impact.

Key Features:

  Tweet Content & Metadata: Includes tweet text, hashtags, mentions, media attachments, and engagement metrics such as likes, retweets, and replies.
  Sentiment Classification: Analyze sentiment polarity (positive, negative, neutral) to gauge public opinion on brands, events, and trending topics.
  Author & User Insights: Access user details such as username, profile information, follower count, and account verification status.
  Hashtag & Topic Tracking: Identify trending hashtags and keywords to monitor conversations and sentiment shifts over time.
  Engagement Metrics: Measure tweet performance based on likes, shares, and comments to evaluate audience interaction.
  Historical & Real-Time Data: Choose from historical datasets for trend analysis or real-time data for up-to-date sentiment tracking.


Use Cases:

  Brand Monitoring & Reputation Management: Track public sentiment around brands, products, and services to manage reputation and customer perception.
  Market Research & Consumer Insights: Analyze consumer opinions on industry trends, competitor performance, and emerging market opportunities.
  Political & Social Sentiment Analysis: Evaluate public opinion on political events, social movements, and global issues.
  AI & Machine Learning Applications: Train sentiment analysis models for natural language processing (NLP) and predictive analytics.
  Advertising & Campaign Performance: Measure the effectiveness of marketing campaigns by analyzing audience engagement and sentiment.



  Our dataset is available in multiple formats (JSON, CSV, Excel) and can be delivered via API, cloud storage (AWS, Google Cloud, Azure), or direct download. 
  Gain valuable insights into social media sentiment and enhance your decision-making with high-quality, structured Twitter data.

The social media analytic market size is projected to grow from USD 11.38 billion in 2025 to USD107.3 billion by 2035, representing a CAGR of 25.16% during the forecast period till 2035.

Myanmar Social Media Sentiment Analysis Dataset

A Myanmar language dataset for sentiment analysis of social media content, translated from an English source dataset.

  Dataset Description

This dataset contains social media text with sentiment annotations translated into Myanmar language. It is derived from the original Social Media Sentiments Analysis Dataset on Kaggle, with texts professionally translated to Myanmar language while preserving the sentiment labels.… See the full description on the dataset page: https://huggingface.co/datasets/chuuhtetnaing/myanmar-social-media-sentiment-analysis-dataset.

The global social media data collection platform market is experiencing robust growth, driven by the increasing reliance on social media for brand monitoring, consumer insights, and competitive analysis. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $45 billion by 2033. This growth is fueled by several key factors. Firstly, the ever-expanding volume of social media data necessitates sophisticated platforms capable of efficient collection, analysis, and interpretation. Secondly, businesses across various sectors are increasingly adopting data-driven strategies, leveraging social listening to understand customer sentiment, identify emerging trends, and improve brand reputation management. Thirdly, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of these platforms, enabling more accurate sentiment analysis, topic modeling, and predictive analytics. This is driving adoption across a broader range of businesses, from small startups to large multinational corporations. However, market growth is not without its challenges. The increasing complexity of social media algorithms and the need to navigate evolving data privacy regulations pose significant hurdles. Furthermore, the market is characterized by intense competition among established players like Hootsuite, Sprout Social, Brandwatch, Talkwalker, Synthesio, BuzzSumo, Google, and Sprinklr, leading to price pressures and the need for continuous innovation. The segmentation of the market reflects the diverse needs of users, ranging from basic social listening tools to highly advanced analytics platforms with integrated CRM capabilities. Geographic expansion, particularly in emerging markets with growing social media penetration, presents a significant opportunity for market players to capitalize on. Successful players will need to focus on providing robust data security measures, staying ahead of algorithm changes, and offering intuitive and user-friendly interfaces to maintain their competitive edge.

The AI social media monitoring tools market is experiencing robust growth, driven by the increasing need for businesses and organizations to understand and manage their online reputation. The market, currently estimated at $5 billion in 2025, is projected to expand significantly over the next decade, fueled by a compound annual growth rate (CAGR) of 20%. This growth is attributable to several key factors. Firstly, the proliferation of social media platforms and the consequent surge in user-generated content necessitates sophisticated tools for efficient monitoring and analysis. Secondly, advancements in artificial intelligence, particularly in natural language processing (NLP) and machine learning (ML), are enhancing the capabilities of these tools, enabling deeper insights and more effective sentiment analysis, user portrait creation, and keyword identification. Thirdly, the rising demand for proactive brand management and crisis communication across various sectors – including enterprise, government agencies, and other organizations – is driving adoption. Segment-wise, sentiment analysis tools currently hold a significant market share, followed by user portrait and keyword identification tools, with others showing promising growth potential. Geographic distribution shows a strong concentration in North America and Europe, though Asia Pacific is emerging as a rapidly expanding market. However, certain restraints limit market expansion. The high cost of implementation and maintenance of AI-powered monitoring tools can be a barrier for smaller businesses. Furthermore, concerns regarding data privacy and security, along with the challenges of integrating these tools into existing workflows, pose significant hurdles. Despite these challenges, the long-term outlook remains positive, with continued innovation in AI and growing awareness of the importance of social media intelligence expected to propel market growth. The competitive landscape is diverse, featuring both established players and emerging innovative companies, leading to healthy competition and continuous improvement in the offerings. This dynamic environment guarantees a future where AI social media monitoring tools play an even more crucial role in shaping online brand perception and strategic decision-making.

This dataset is structured as a graph, where nodes represent users and edges capture their interactions, including tweets, retweets, replies, and mentions. Each node provides detailed user attributes, such as unique ID, follower and following counts, and verification status, offering insights into each user's identity, role, and influence in the mental health discourse. The edges illustrate user interactions, highlighting engagement patterns and types of content that drive responses, such as tweet impressions. This interconnected structure enables sentiment analysis and public reaction studies, allowing researchers to explore engagement trends and identify the mental health topics that resonate most with users.

The dataset consists of three files: 1. Edges Data: Contains graph data essential for social network analysis, including fields for UserID (Source), UserID (Destination), Post/Tweet ID, and Date of Relationship. This file enables analysis of user connections without including tweet content, maintaining compliance with Twitter/X’s data-sharing policies. 2. Nodes Data: Offers user-specific details relevant to network analysis, including UserID, Account Creation Date, Follower and Following counts, Verified Status, and Date Joined Twitter. This file allows researchers to examine user behavior (e.g., identifying influential users or spam-like accounts) without direct reference to tweet content. 3. Twitter/X Content Data: This file contains only the raw tweet text as a single-column dataset, without associated user identifiers or metadata. By isolating the text, we ensure alignment with anonymization standards observed in similar published datasets, safeguarding user privacy in compliance with Twitter/X's data guidelines. This content is crucial for addressing the research focus on mental health discourse in social media. (References to prior Data in Brief publications involving Twitter/X data informed the dataset's structure.)

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Sentiment Analytics Software Market Size 2025-2029

The sentiment analytics software market size is forecast to increase by USD 2.34 billion, at a CAGR of 16.6% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing use of digital communication channels and the rising adoption of social media. With the expanding internet penetration, businesses and organizations are leveraging sentiment analytics software to monitor and analyze customer opinions and feedback in real-time. This data-driven approach enables companies to gain valuable insights into customer preferences, improve brand reputation, and make informed business decisions. However, the integration of generative AI in sentiment analytics poses a challenge. While AI-powered sentiment analysis offers enhanced accuracy and efficiency, it also introduces context-dependent errors. Misinterpretation of sarcasm, idioms, and cultural nuances can lead to inaccurate analysis and potential negative consequences for businesses. To navigate this challenge, companies must invest in advanced AI models that can better understand the nuances of human language and context. By doing so, they can capitalize on the opportunities presented by sentiment analytics software and effectively manage their online reputation and customer relationships.

What will be the Size of the Sentiment Analytics 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.
Request Free SampleThe market continues to evolve, driven by the increasing demand for real-time customer feedback analysis and business intelligence (BI) applications. This market encompasses various technologies, including text mining, machine learning (ML), deep learning (DL), and natural language processing (NLP), to extract insights from unstructured data. Applications of sentiment analytics span across multiple sectors, such as marketing campaign effectiveness, competitive intelligence, risk management, brand reputation management, and customer experience optimization. Real-time sentiment monitoring is a crucial aspect of these applications, enabling businesses to respond promptly to customer feedback and mitigate potential crises. Moreover, sentiment analytics plays a pivotal role in market research, providing valuable insights into consumer opinions and preferences. Polarity detection, emotion recognition, and topic modeling are essential components of sentiment analysis algorithms, helping to identify trends and patterns in customer sentiment. Model performance evaluation metrics, such as false positive rate, false negative rate, and F1 score, are crucial in ensuring the accuracy and reliability of sentiment analytics models. Additionally, bias detection is becoming increasingly important to mitigate potential biases in data and improve model fairness. The ongoing development of sentiment analytics is fueled by advancements in ML and DL, enabling more sophisticated models and improved accuracy. Social media monitoring is a significant area of growth, with businesses leveraging social listening to gain insights into customer sentiment and engagement. Data governance and ethical considerations are essential aspects of sentiment analytics, ensuring that data is collected, processed, and used ethically and transparently. Sentiment analysis APIs and integration with data visualization tools further enhance the value of sentiment analytics, making it an indispensable tool for businesses seeking to gain a competitive edge and improve customer experience.

How is this Sentiment Analytics Software Industry segmented?

The sentiment analytics 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. DeploymentOn-premisesCloud-basedEnd-userRetailBFSIHealthcareOthersGeographyNorth AmericaUSEuropeGermanyUKAPACChinaIndiaRest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.In the realm of business intelligence, on-premises deployment of sentiment analytics software has emerged as a preferred choice for organizations seeking control over their data and operations. This setup enables companies to manage their data privately and adhere to regulatory requirements. On-premises sentiment analytics solutions offer customization benefits, allowing businesses to tailor the software to their unique needs and seamlessly integrate it with existing systems. Furthermore, dedicated on-premises infrastructure results in superior performance and faster processing times. Sentiment analytics software employs advanced techniques such as public opinion tracking

This is a data set of 482,251 public tweets and retweets (Twitter IDs) posted by the #edchat online community of educators who discuss current trends in teaching with technology. The data set was collected via Twitter's Streaming API between Feb 1, 2018 and Apr 4, 2018, and was used as part of the research on developing a learning analytics dashboard for teaching and learning with Twitter. Following Twitter's terms of service, the data set only includes unique identifiers of relevant tweets. To collect the actual tweets that are part of this data set, you will need to use one of the available third party tools such as Hydrator or Twarc ("hydrate" function). As part of this release, we are also attaching an enriched version of this data set that contains sentiment and opinion analysis labels that were produced by analyzing each tweet with the help of the NLTK SentimentAnalyzer Python package. *This work was supported in part by eCampusOntario and The Social Sciences and Humanities Research Council of Canada.

This data set consists of approximately 1.64 Million Arabic tweets (shared by their IDs) posted from 2009 to 2020, and their corresponding sentiment using a three-point classification system of Positive, Negative and Neutral/Mixed. No specific locations and/or keywords were specified throughout the data collection to obtain variation in the dialects and topics represented within the dataset. It is important to note that any biases in the proposed dataset in relation to the dialects and/or topics discussed were unintentional.

Please use the following citation if you use this data in a paper:

Abdaljalil, S., Hassanein, S., Mubarak, H., & Abdelali, A. (2023). Towards Generalization of Machine Learning Models: A Case Study of Arabic Sentiment Analysis. Proceedings of the International AAAI Conference on Web and Social Media, 17(1), 971-980.

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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

The present dataset contains Tweets in any language supported by Twitter obtained during the months January to March 2023, with any mention to the topic CCS/CCUS. The scraping process were done in Python, using the official Twitter API. All tweets were manually annotated after being machine translated into English.

- Structure
Every row contains:
1st cell (A): Language
2nd cell (B): Tweet-text
3rd cell (Cc: Benefit
4th cell (D): Concern
5th cell (E): Perception – Fight climate change
6th cell (F): Perception – Climate-friendly technology
7th cell (G): Perception – Extensive R&D needed
8th cell (H): Perception – Better options than CCS
9th cell (I): Sentiment
10th cell (J): Relatedness
11th cell (K): Comments

- Annotations
Benefit
Preventing c. change
Reducing c. change risks
Safeguarding jobs
Creating new jobs
Fossil energy production envir. friendly
Products envir. friendly
Reducing envir. impact
Other
None
Concern
Accidents
Leakages
Environmental
Earthquake-related
Increased local traffic
Investment
Greenwashing
Lock-in effects for fossil energy
Increase cost
Other
None
Perception (Yes / No / None)
Fight climate change
Climate-friendly technology
Extensive R&D needed
Better options than CCS
Sentiment
Positive
Negative
Neutral

Sentiment Analysis Dataset

  Overview

This dataset is designed for sentiment analysis tasks, providing labeled examples across three sentiment categories:

0: Negative 1: Neutral 2: Positive

It is suitable for training, validating, and testing text classification models in tasks such as social media sentiment analysis, customer feedback evaluation, and opinion mining.

  Dataset Details







  Key Features

Type: CSV Language: English Labels: 0:… See the full description on the dataset page: https://huggingface.co/datasets/syedkhalid076/Sentiment-Analysis.

This is a project on Social Media Sentiment Analysis using Hortonworks Sandbox following the procedure provided at website. The default username and password is root and clickstream respectively. Any BI tool can be used but I recommend Tableau which can be downloaded from website. Any user can contact me at cmdude16@gmail.com for further guidance.