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 the following challenges:

Processing vast amounts of unstructured text data from multiple platforms like Twitter, Facebook, and Instagram.

Accurately interpreting slang, emojis, and nuanced language used by social media users.

Identifying trends and actionable insights in real-time to respond to potential crises or opportunities effectively.

Solution Provided

An advanced sentiment analysis system was developed using Natural Language Processing (NLP) and sentiment analysis algorithms. The solution was designed to:

Classify social media posts into positive, negative, and neutral sentiments.

Extract key topics and trends related to the brand and its products.

Provide real-time dashboards for monitoring customer sentiment and identifying areas of improvement.

Development Steps

Data Collection

Aggregated data from major social media platforms using APIs, focusing on brand mentions, hashtags, and product keywords.

Preprocessing

Cleaned and normalized text data, including handling slang, emojis, and misspellings, to prepare it for analysis.

Model Training

Trained NLP models for sentiment classification using supervised learning. Implemented topic modeling algorithms to identify recurring themes and discussions.

Validation

Tested the sentiment analysis models on labeled datasets to ensure high accuracy and relevance in classifying social media posts.

Deployment

Integrated the sentiment analysis system with a real-time analytics dashboard, enabling the marketing and customer support teams to track trends and respond proactively.

Monitoring & Improvement

Established a continuous feedback mechanism to refine models based on evolving language patterns and new social media trends.

Results

Gained Actionable Insights

The system provided detailed insights into customer opinions, helping the company identify strengths and areas for improvement.

Improved Brand Reputation Management

Real-time monitoring enabled swift responses to negative feedback, mitigating potential reputation risks.

Informed Marketing Strategies

Insights from sentiment analysis guided targeted marketing campaigns, resulting in higher engagement and ROI.

Enhanced Customer Relationships

Proactive engagement with customers based on sentiment analysis improved customer satisfaction and loyalty.

Scalable Monitoring Solution

The system scaled efficiently to analyze data across multiple languages and platforms, broadening the company’s reach and understanding.

General Description

This dataset comprises 4,038 tweets in Spanish, related to discussions about artificial intelligence (AI), and was created and utilized in the publication "Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights," (10.1109/IE61493.2024.10599899) presented at the 20th International Conference on Intelligent Environments. It is designed to support research on public perception, sentiment, and engagement with AI topics on social media from a Spanish-speaking perspective. Each entry includes detailed annotations covering sentiment analysis, user engagement metrics, and user profile characteristics, among others.

Data Collection Method

Tweets were gathered through the Twitter API v1.1 by targeting keywords and hashtags associated with artificial intelligence, focusing specifically on content in Spanish. The dataset captures a wide array of discussions, offering a holistic view of the Spanish-speaking public's sentiment towards AI.

Dataset Content

ID: A unique identifier for each tweet.

text: The textual content of the tweet. It is a string with a maximum allowed length of 280 characters.

polarity: The tweet's sentiment polarity (e.g., Positive, Negative, Neutral).

favorite_count: Indicates how many times the tweet has been liked by Twitter users. It is a non-negative integer.

retweet_count: The number of times this tweet has been retweeted. It is a non-negative integer.

user_verified: When true, indicates that the user has a verified account, which helps the public recognize the authenticity of accounts of public interest. It is a boolean data type with two allowed values: True or False.

user_default_profile: When true, indicates that the user has not altered the theme or background of their user profile. It is a boolean data type with two allowed values: True or False.

user_has_extended_profile: When true, indicates that the user has an extended profile. An extended profile on Twitter allows users to provide more detailed information about themselves, such as an extended biography, a header image, details about their location, website, and other additional data. It is a boolean data type with two allowed values: True or False.

user_followers_count: The current number of followers the account has. It is a non-negative integer.

user_friends_count: The number of users that the account is following. It is a non-negative integer.

user_favourites_count: The number of tweets this user has liked since the account was created. It is a non-negative integer.

user_statuses_count: The number of tweets (including retweets) posted by the user. It is a non-negative integer.

user_protected: When true, indicates that this user has chosen to protect their tweets, meaning their tweets are not publicly visible without their permission. It is a boolean data type with two allowed values: True or False.

user_is_translator: When true, indicates that the user posting the tweet is a verified translator on Twitter. This means they have been recognized and validated by the platform as translators of content in different languages. It is a boolean data type with two allowed values: True or False.

Cite as

Guerrero-Contreras, G., Balderas-Díaz, S., Serrano-Fernández, A., & Muñoz, A. (2024, June). Enhancing Sentiment Analysis on Social Media: Integrating Text and Metadata for Refined Insights. In 2024 International Conference on Intelligent Environments (IE) (pp. 62-69). IEEE.

Potential Use Cases

This dataset is aimed at academic researchers and practitioners with interests in:

Sentiment analysis and natural language processing (NLP) with a focus on AI discussions in the Spanish language.

Social media analysis on public engagement and perception of artificial intelligence among Spanish speakers.

Exploring correlations between user engagement metrics and sentiment in discussions about AI.

Data Format and File Type

The dataset is provided in CSV format, ensuring compatibility with a wide range of data analysis tools and programming environments.

License

The dataset is available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, permitting sharing, copying, distribution, transmission, and adaptation of the work for any purpose, including commercial, provided proper attribution is given.

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.

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.

Market Overview:

The United States social media analytics market size is projected to exhibit a growth rate (CAGR) of 18.30% during 2025-2033. The increasing utilization of social media by users, rising emphasis on personalized marketing strategies, the widespread integration of artificial intelligence (AI) and machine learning (ML), and the burgeoning awareness about the importance of customer feedback represent some of the key factors driving the market.

Social media analytics refers to the process of gathering, analyzing, and interpreting data from social media platforms to understand online interactions and trends. They combine advanced analytics techniques, like text analysis and sentiment analysis, with user engagement metrics to provide insights into social media behavior. Social media analytics utilize algorithms, artificial intelligence (AI), and machine learning (ML) to process vast amounts of unstructured social media data. They include various types, such as descriptive, diagnostic, predictive, and prescriptive analysis, designed to manage large data volumes from various platforms. Social media analytics are utilized in various applications, including market research, customer service, public relations, sentiment analysis, trend analysis, competitive analysis, influencer identification, brand monitoring, campaign performance, and content optimization. They aid in enhancing customer insights, improving marketing strategies, providing real-time feedback, increasing return on investment (ROI), supporting crisis management, tracking audience engagement, and managing brand reputation. Furthermore, social media analytics are known for their data-driven decision-making, cost-effectiveness, scalability, versatility, accessibility, real-time analysis, user-friendliness, customizability, and comprehensive data visualization.

United States Social Media Analytics Market Trends:

The increasing utilization of social media by users, leading to the demand for advanced analytics tools capable of handling large and complex datasets, is fostering the market growth. Besides this, the rising emphasis on personalized marketing strategies, as companies leverage social media analytics to tailor their marketing efforts, is providing a thrust to the market growth. Along with this, the widespread integration of artificial intelligence (AI) and machine learning (ML) in social media analytics tools, enabling more sophisticated data processing and insight generation, is creating a positive outlook for the market growth. In line with this, the growing adoption of technologies that facilitate the analysis of unstructured data, sentiment analysis, and predictive modeling, providing businesses with actionable insights to form their strategies, is favoring the market growth. Apart from this, the burgeoning awareness about the importance of customer feedback in shaping business strategies is enhancing the market growth. Furthermore, the increasing adoption of social media analytics tools by companies to monitor customer opinions and feedback in real-time, allowing them to respond to consumer needs and market changes quickly, is acting as a growth-inducing factor. Along with this, the heightened investment in digital marketing, as businesses allocate more resources to online platforms, prompting the need for robust analytics tools, is providing a thrust to the market growth. In addition to this, the rising integration of social media analytics with other business intelligence tools, providing a more holistic view of the customer's journey, is offering lucrative growth opportunities for the market.

United States Social Media Analytics Market Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2025-2033. Our report has categorized the market based on component, deployment mode, organization size, application, and end user.

Component Insights:

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• Solutions
• Services
  

The report has provided a detailed breakup and analysis of the market based on the component. This includes solutions and services.

Deployment Mode Insights:

• On-premises
• Cloud-based
  

A detailed breakup and analysis of the market based on deployment mode have also been provided in the report. This includes on-premises and cloud-based.

Organization Size Insights:

• Small and Medium-sized Enterprises
• Large Enterprises
  

The report has provided a detailed breakup and analysis of the market based on the organization size. This includes small and medium-sized enterprises and large enterprises.

Application Insights:

• Customer Segmentation and Targeting
• Competitor Benchmarking
• Multichannel Campaign Management
• Customer Behavioral Analysis
• Marketing Management
  

A detailed breakup and analysis of the market based on application have also been provided in the report. This includes customer segmentation and targeting, competitor benchmarking, multichannel campaign management, customer behavioral analysis, and marketing management.

End User Insights:

• BFSI
• Media and Entertainment
• Travel and Hospitality
• IT and Telecom
• Retail
• Healthcare
• Others
  

The report has provided a detailed breakup and analysis of the market based on the end user. This includes BFSI, media and entertainment, travel and hospitality, IT and telecom, retail, healthcare, and others.

Regional Insights:

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• Northeast
• Midwest
• South
• West
  

The report has also provided a comprehensive analysis of all the major regional markets, which include the Northeast, Midwest, South, and West.

Competitive Landscape:

The market research report has also provided a comprehensive analysis of the competitive landscape in the market. Competitive analysis such as market structure, key player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles of all major companies have been provided.

United States Social Media Analytics Market Report Coverage:

The global Social Media Analytics Market is estimated to reach USD 9,633.6 million by 2026, growing at a CAGR of 27.1% from 2022 to 2026. The market is driven by the increasing adoption of social media platforms by businesses to connect with their target audience and gain insights into their behavior. The benefits of social media analytics include improved customer engagement, increased brand awareness, and optimized marketing campaigns. Social media analytics refers to the process of drawing conclusions from social platforms for business and organizational purposes. It involves analyzing likes, shares, comments, and trends to understand audience reactions and tendencies. Through these measures, firms and organisations can fine-tune promotional tactics, improve customer relations, and gauge brand image. Social media monitoring tools such as sentiment analysis and demographic profiling also assist in data interpretation to establish who and what is popular. Real-time monitoring and tracking allow the necessary changes in strategies to be made, which can be highly beneficial in competitive environments. Other key considerations are the right to privacy and protection of user data, which makes it imperative to uphold responsible use of user data.

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.

Researcher(s): Alexandros Mokas, Eleni Kamateri

Supervisor: Ioannis Tsampoulatidis

This repository contains 3 social media datasets:

2 Post-processing datasets: These datasets contain post-processing data extracted from the analysis of social media posts collected for two different use cases during the first two years of the Deepcube project. More specifically, these include:

• The UC2 dataset containing the post-processing analysis of the Twitter data collected for the DeepCube use case (UC2) dealing with the climate induced migration in Africa. This dataset contains in total 5,695,253 social media posts collected from the Twitter platform, based on the initial version of search criteria relevant to UC2 defined by Universitat De Valencia, focused on the regions of Ethiopia and Somalia and started from 26 June, 2021 till March, 2023.
• The UC5 dataset containing the post-processing analysis of the Twitter and Instagram data collected for the DeepCube use case (UC5) related to the sustainable and environmentally-friendly tourism. This dataset contains in total 58,143 social media posts collected from the Twitter and Instagram platform (12,881 collected from Twitter and 45,262 collected from Instagram), based on the initial version of search criteria relevant to UC5 defined by MURMURATION SAS, focused on the regions of Brasil and started from 26 June, 2021 till March, 2023.

1 Annotated dataset: An additional anottated dataset was created that contains post-processing data along with annotations of Twitter posts collected for UC2 for the years 2010-2022. More specifically, it includes:

• The UC2 dataset contain the post-processing of the Twitter data collected for the DeepCube use case (UC2) dealing with the climate induced migration in Africa. This dataset contains in total 1721 annotated (412 relevant and 1309 irrelevant) by social media posts collected from the Twitter platform, focused on the region of Somalia and started from 1 January, 2010 till 31 December, 2022.

For every social media post retrieved from Twitter and Instagram, a preprocessing step was performed. This involved a three-step analysis of each post using the appropriate web service. First, the location of the post was automatically extracted from the text using a location extraction service. Second, the images included in the post were analyzed using a concept extraction service, which identified and provided the top ten concepts that best described the image. These concepts included items such as "person," "building," "drought," "sun," and so on. Finally, the sentiment expressed in the post's text was determined by using a sentiment analysis service. The sentiment was classified as either positive, negative, or neutral.

After the social media posts were preprocessed, they were visualized using the Social Media Web Application. This intuitive, user-friendly online application was designed for both expert and non-expert users and offers a web-based user interface for filtering and visualizing the collected social media data. The application provides various filtering options, an interactive map, a timeline, and a collection of graphs to help users analyze the data. Moreover, this application provides users with the option to download aggregated data for specific periods by applying filters and clicking the "Download Posts" button. This feature allows users to easily extract and analyze social media data outside of the web application, providing greater flexibility and control over data analysis.

The dataset is provided by INFALIA.

INFALIA, being a spin-off of the CERTH institute and a partner of a research EU project, releases this dataset containing Tweets IDs and post pre-processing data for the sole purpose of enabling the validation of the research conducted within the DeepCube. Moreover, Twitter Content provided in this dataset to third parties remains subject to the Twitter Policy, and those third parties must agree to the Twitter Terms of Service, Privacy Policy, Developer Agreement, and Developer Policy (https://developer.twitter.com/en/developer-terms) before receiving this download.

Snapshot img

What is the Sentiment Analytics Software Market Size?

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 due to the increasing use of social media and the rising internet penetration in North America. Businesses are leveraging sentiment analysis to gain insights into customer opinions and feedback. A key trend in the market is the integration of generative AI to improve the accuracy and context-dependence of sentiment analysis. However, challenges such as context-dependent errors and the need for large amounts of data to train AI models persist. To stay competitive, market participants must focus on addressing these challenges and continuously improving the accuracy and reliability of their sentiment analysis solutions. This market analysis report provides an in-depth examination of the growth drivers, trends, and challenges shaping the sentiment analytics software market.

What will be the size of Market during the forecast period?

Request Free Sentiment Analytics Software Market Sample

Market Segmentation

The market 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.

Deployment

  On-premises
  Cloud-based


End-user

  Retail
  BFSI
  Healthcare
  Others


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    India


  South America



  Middle East and Africa

Which is the largest segment driving market growth?

The on-premises segment is estimated to witness significant growth during the forecast period. In the realm of data analysis, sentiment analytics software plays a pivotal role in understanding public perception toward brands, services, and entities. For organizations in the healthcare sector, reputation management is of utmost importance. Sentiment analytics software deployed on-premises offers several benefits. With on-premises deployment, organizations retain complete control over their data, ensuring privacy and compliance with healthcare regulations. This setup allows for customization to meet specific business needs and seamless integration with existing systems.

Get a glance at the market share of various regions. Download the PDF Sample

The on-premises segment was valued at USD 788.40 million in 2019. Furthermore, the use of dedicated infrastructure results in superior performance and faster processing times. Government institutions, media, telecom, and other industries also reap the benefits of on-premises sentiment analytics software. Data from surveys, social media, and other sources undergoes text analysis to uncover valuable insights. By staying informed of public sentiment, organizations can make data-driven decisions, respond to crises, and improve their offerings. Sentiment analysis is not limited to text data from surveys and social media. Media mentions and customer interactions through phone and email are also valuable sources of data. By harnessing the power of on-premises sentiment analytics software, organizations can gain a competitive edge and maintain a strong reputation.

Which region is leading the market?

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

North America is estimated to contribute 38% 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. In North America, sentiment analytics software has gained significant traction due to the region's high internet penetration and prioritization of enhancing customer experiences. By 2024, internet usage in North America reached nearly 97%, creating a solid base for the implementation of sentiment analysis tools. Companies in the US and Canada are investing heavily in advanced technologies to personalize customer interactions and improve overall satisfaction.

Further, Natural Language Processing (NLP) plays a crucial role in sentiment analysis, enabling businesses to understand and respond effectively to customer opinions. By staying attuned to customer sentiments, North American businesses can foster brand reputation, enhance customer satisfaction, and make data-driven decisions.

How do company ranking index and market positioning come to your aid?

Companies are implementing various strategies, such as strategic alliances, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the market.

Alphabet Inc.: The company offers sentiment analytics software that supports multiple languages and can be integrated into various applications for real-time analysis.

The research project associated with this dataset focuses on the analysis of the top threads within the ddo subreddit. The dataset contains essential information about each of these threads, including the author's username, the post's title, the post text, its score, and the number of comments it has received. Additionally, it includes a detailed record of all comments within each thread, encompassing the commenter's username, the date and time of their comment, and the score received by each comment.
The purpose of this project is to recognize addicted users within the ddo subreddit community by considering their activity patterns, emotional expressions, and content preferences, ultimately contributing to a deeper understanding of addiction-related behaviors in online communities and informing strategies for tailored support and interventions.

The dataset contains a collection of articles related to Virtual Influencers and Public Perception: Social Media Sentiment Analysis and A Comprehensive Bibliometric in the Scopus database.

Please cite the following paper when using this dataset:

N. Thakur, V. Su, M. Shao, K. Patel, H. Jeong, V. Knieling, and A. Bian “A labelled dataset for sentiment analysis of videos on YouTube, TikTok, and other sources about the 2024 outbreak of measles,” Proceedings of the 26th International Conference on Human-Computer Interaction (HCII 2024), Washington, USA, 29 June - 4 July 2024. (Accepted as a Late Breaking Paper, Preprint Available at: https://doi.org/10.48550/arXiv.2406.07693)

Abstract

This dataset contains the data of 4011 videos about the ongoing outbreak of measles published on 264 websites on the internet between January 1, 2024, and May 31, 2024. These websites primarily include YouTube and TikTok, which account for 48.6% and 15.2% of the videos, respectively. The remainder of the websites include Instagram and Facebook as well as the websites of various global and local news organizations. For each of these videos, the URL of the video, title of the post, description of the post, and the date of publication of the video are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis (using VADER), subjectivity analysis (using TextBlob), and fine-grain sentiment analysis (using DistilRoBERTa-base) of the video titles and video descriptions were performed. This included classifying each video title and video description into (i) one of the sentiment classes i.e. positive, negative, or neutral, (ii) one of the subjectivity classes i.e. highly opinionated, neutral opinionated, or least opinionated, and (iii) one of the fine-grain sentiment classes i.e. fear, surprise, joy, sadness, anger, disgust, or neutral. These results are presented as separate attributes in the dataset for the training and testing of machine learning algorithms for performing sentiment analysis or subjectivity analysis in this field as well as for other applications. The paper associated with this dataset (please see the above-mentioned citation) also presents a list of open research questions that may be investigated using this dataset.

Replication folder for "Chinese Investment and Elite Sentiment in Southeast Asia: An Event Study of Influence Along the Belt and Road". Please cite the dataset as "Sun, Yining, Kapstein, Ethan, & Shapiro, Jacob (2024). Chinese investment and elite sentiment in Southeast Asia: An event study of influence along the belt and road. Research & Politics, 11(1). https://doi.org/10.1177/20531680231222988"

Usecase/Applications possible with the data:

Customer feedback analysis: Analyzing customer feedback can be helpful for businesses to keep customers happy, stay loyal to the brand, and identify any areas to improve.

Social media monitoring: With sentiment analysis, companies can monitor what's being said about them on social media and use that to figure out how people feel about their products and services and track any new trends.

Market research: Sentiment analysis can be used to analyze market trends and consumer preferences, which can help companies make informed business decisions and develop effective marketing strategies.

Financial analysis: You can use sentiment analysis to determine what people say about the stock market through news and social media, which can help you make investing decisions.

For e-commerce (amazon/Bestbuy/home depot and much more) following data fields can be included: Title Price Vendor Name Ratings Reviews Brand ASIN URL Sentiment analysis for each review And other fields, as per request

Dataset

This dataset was created by Subhashini

Contents

Summary of the Twitter dataset used in this study.

A Russian-language sentiment lexicon for social media discussions on political and social issues.

The file contains raw markings collected with LINIS coding service https://linis-crowd.org [in Russian].

Learn more about PolSentiLex in our papers:

Koltsova, O., & Alexeeva, S. (2015). Linis-crowd.org: A lexical resource for Russian sentiment analysis of social media [Linis-crowd.org: Lexichesk resurs dl’a analiza tonal’nosti sotsial’no-politicheskix tekstov]. Computational Linguis- Tics and Computantional Ontologies: Proceedings of the XVIII Joint Conference “Internet and Modern Society (IMS-2015)” [Kompyuternaya Lingvistika i Vyichis- Litelnyie Ontologii: Sbornik Nauchnyih Statey. Trudyi XVIII Ob’edinennoy Konferen- Tsii «Internet i Sovremennoe Obschestvo» (IMS-2015)], 25–34. [in Russian] URL: https://scila.hse.ru/data/2020/06/02/1603986481/koltsovaoyuetal.pdf

Koltsova, O., Alexeeva, S., & Koltsov, S. (2016). An Opinion Word Lexicon and a Training Dataset for Russian Sentiment Analysis of Social Media. Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2016”, 277–287. URL: http://www.dialog-21.ru/media/3400/koltsovaoyuetal.pdf

Koltsova O., Alexeeva S., Pashakhin S., Koltsov S. (2020) PolSentiLex: Sentiment Detection in Socio-Political Discussions on Russian Social Media. In: Filchenkov A., Kauttonen J., Pivovarova L. (eds) Artificial Intelligence and Natural Language. AINL 2020. Communications in Computer and Information Science, vol 1292. Springer, Cham. https://doi.org/10.1007/978-3-030-59082-6_1

Dataset

This dataset was created by Blesson Densil

Released under Data files © Original Authors

Contents

Sentiment Analytics Software Market size was valued at USD 3.17 Billion in 2024 and is projected to reach USD 10.5 Billion by 2031, growing at a CAGR of 14.9% from 2024 to 2031.

Sentiment Analytics Software Market Drivers

Growth in Social Media Usage: As social media platforms are used more often for consumer engagement, communication, and brand promotion, there is a growing need for sentiment analytics software to track, examine, and extract insights from social media posts, comments, and feedback.

consumer Experience Management: In order to better understand consumer attitudes, preferences, and comments across a variety of channels, organizations place a high priority on customer experience management and sentiment analysis. This has led to the development of sentiment analytics software in an effort to increase customer happiness and loyalty.

Brand Reputation Management: The use of sentiment analytics software for brand monitoring, sentiment tracking, and reputation management is driven by the need to handle possible PR crises, maintain a positive brand sentiment, and monitor and manage brand reputation in real-time.

There is an increasing demand for sentiment analysis of text from social media which are mostly code-mixed. Systems trained on monolingual data fail for code-mixed data due to the complexity of mixing at different levels of the text. However, very few resources are available for code-mixed data to create models specific for this data. Although much research in multilingual and cross-lingual sentiment analysis has used semi-supervised or unsupervised methods, supervised methods still performs better. Only a few datasets for popular languages such as English-Spanish, English-Hindi, and English-Chinese are available. There are no resources available for Malayalam-English code-mixed data. This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators. This gold standard corpus obtained a Krippendorff’s alpha above 0.8 for the dataset. We use this new corpus to provide the benchmark for sentiment analysis in Malayalam-English code-mixed texts.