Goto

Collaborating Authors

 twitter sentiment


Enhancing Cryptocurrency Sentiment Analysis with Multimodal Features

Liu, Chenghao, Mahanti, Aniket, Naha, Ranesh, Wang, Guanghao, Sbai, Erwann

arXiv.org Artificial Intelligence

As cryptocurrencies gain popularity, the digital asset marketplace becomes increasingly significant. Understanding social media signals offers valuable insights into investor sentiment and market dynamics. Prior research has predominantly focused on text-based platforms such as Twitter. However, video content remains underexplored, despite potentially containing richer emotional and contextual sentiment that is not fully captured by text alone. In this study, we present a multimodal analysis comparing TikTok and Twitter sentiment, using large language models to extract insights from both video and text data. We investigate the dynamic dependencies and spillover effects between social media sentiment and cryptocurrency market indicators. Our results reveal that TikTok's video-based sentiment significantly influences speculative assets and short-term market trends, while Twitter's text-based sentiment aligns more closely with long-term dynamics. Notably, the integration of cross-platform sentiment signals improves forecasting accuracy by up to 20%.


The Impact of Twitter Sentiments on Stock Market Trends

Mokhtari, Melvin, Seraj, Ali, Saeedi, Niloufar, Karshenas, Adel

arXiv.org Artificial Intelligence

The Web is a vast virtual space where people can share their opinions, impacting all aspects of life and having implications for marketing and communication. The most up-to-date and comprehensive information can be found on social media because of how widespread and straightforward it is to post a message. Proportionately, they are regarded as a valuable resource for making precise market predictions. In particular, Twitter has developed into a potent tool for understanding user sentiment. This article examines how well tweets can influence stock symbol trends. We analyze the volume, sentiment, and mentions of the top five stock symbols in the S&P 500 index on Twitter over three months. Long Short-Term Memory, Bernoulli Na\"ive Bayes, and Random Forest were the three algorithms implemented in this process. Our study revealed a significant correlation between stock prices and Twitter sentiment.


Twitter Sentiment: Bears at Seahawks, Week 16, 2021

#artificialintelligence

We've been doing a lot of NLP Sentiment Analysis on NFL games recently. So far, the team with the higher pregame Twitter sentiment has won 4 out of 10 analyses with 2 Week 16 games finished at the time of writing: Lions at Falcons, and Chargers at Texans. For week 16, we're going to analyze all the games and…