social-media-intelligence

You can extract financial sentiment signals by collecting social media data and applying VADER or FinBERT models to generate sentiment scores for trading strategies. This Skill processes messages from Twitter, Reddit, Discord, and Telegram to provide actionable market sentiment data.

Calculating a fear and greed index from social media buzz involves tracking message volume, unique authors, and applying Z-scored buzz metrics. This Skill constructs a fear/greed indicator by aggregating discussion data across multiple platforms to gauge overall market sentiment.

Yes, you can use FinBERT and VADER together for sentiment analysis on Telegram messages. This Skill utilizes both models alongside LLM-based scoring to interpret text from Telegram and other platforms, distinguishing between institutional and retail sentiment.

Yes, you need social media API access to analyze market sentiment with Python. This Skill requires API access for platforms like Twitter and Reddit, alongside Python libraries like tweepy, praw, and discord.py, to gather financial signals and perform data processing.

To distinguish between institutional and retail sentiment, this Skill analyzes discussion patterns and message characteristics across platforms. It processes social media data to differentiate market participant types, providing more informed sentiment-driven trading decisions.

Limitations of using social media sentiment for trading include reliance on platform API access and the need for extensive Python library dependencies. This Skill requires access to social media APIs and libraries like transformers and scipy, which may constrain large-scale or historical data analysis.