Sonic Realtime Datasets

This dataset provides a comprehensive real-time analysis of consumer sentiment surrounding Nike's latest sneaker launch using Twitter data. It captures tweets mentioning the new release, highlighting user reactions, engagement metrics, sentiment scores, and geographic insights. By analyzing public sentiment, this dataset helps brands understand how their products are being received, identify emerging trends, and refine marketing strategies. It is particularly useful for marketing optimization, trend prediction, and brand monitoring, offering valuable insights into customer preferences, product reception, and the overall impact of Nike's latest sneaker release on the market.

This dataset captures the impact of influencers and fan reactions on Twitter during the Real Madrid vs Man City match on February 20, 2025. It includes tweets from verified accounts, sports analysts, and regular fans, covering match updates, tactical analysis, and emotional responses. The data includes tweet metadata such as sentiment scores, engagement metrics (likes, retweets, replies), hashtags, and media attachments, making it valuable for social media analysis, sentiment tracking, and sports engagement research

This dataset aggregates Twitter data focused on financial market sentiment analysis. It includes tweets discussing stock tickers, economic news, and market trends along with tweet metadata, engagement metrics, sentiment scores, and user information. The data is ideal for analyzing investor sentiment and modeling market behavior.

This dataset aggregates Twitter data centered on public perception and sentiment toward Donald Trump. It includes tweets from his official account, political analysts, and everyday users commenting on his public image, policies, and controversies. Each tweet entry contains metadata (tweet ID, timestamp, text, language), user details (verification status, follower count, influencer flag), engagement metrics (retweets, likes, replies, quote counts), as well as a computed sentiment score and a topic label. This resource is intended for research on political communication and public opinion analysis.

This dataset aggregates Twitter data focused on fan reviews and sentiment regarding the ICC World Cup from last season. It includes tweets from the official ICC account, cricket analysts, and everyday fans discussing match experiences, team performances, and overall tournament excitement. Each tweet entry includes metadata (tweet ID, timestamp, text, language), user details (username, verified status, follower count), engagement metrics (retweets, likes, replies, quote counts), a computed sentiment score, and a topic label (e.g., official_update, analyst_opinion, fan_reaction). This resource is ideal for researchers studying sports engagement, social media sentiment analysis, and fan-driven narratives in cricket.

This dataset aggregates Twitter data focused on public sentiment and engagement related to Coinbase and its crypto exchange services. It includes tweets from Coinbase’s official account, crypto analysts, and everyday users sharing reviews on service quality, fee structures, platform updates, and customer support experiences. Each tweet entry includes metadata (tweet ID, timestamp, text, language), user details (verification status, follower count, influencer flag), engagement metrics (retweets, likes, replies, quote counts), as well as a computed sentiment score and a topic label. This resource is ideal for researching digital currency exchanges, social media sentiment, and consumer feedback on crypto services.

This dataset aggregates Twitter data focused on brand and product reviews for KFC. It includes tweets from KFC’s official account, food influencers, and regular customers discussing product taste, service, and overall experience. The data features tweet text, timestamps, engagement metrics, sentiment scores, and user metadata—making it ideal for sentiment analysis and social media brand monitoring research.