Define the target assets, time frame, and sentiment labels
Identify relevant X accounts, hashtags, keywords, Telegram channels, and groups
Collect posts, replies, forwards, and message metadata from X and Telegram
Filter spam, bots, duplicate posts, and non-English content if needed
Normalize text by lowercasing, removing URLs, mentions, tickers, and punctuation
Tokenize text and handle slang, abbreviations, emojis, and crypto-specific terms
Detect language and translate if required
Build a crypto-specific sentiment lexicon or use a pretrained sentiment model
Fine-tune the model on labeled crypto social media data
Classify each message as positive, negative, or neutral
Score message intensity and confidence
Aggregate sentiment by coin, topic, channel, and time window
Weight messages by author credibility, engagement, and recency
Track sentiment spikes around news, listings, hacks, and market events
Compare sentiment with price, volume, and volatility data
Visualize sentiment trends, heatmaps, and anomaly alerts
Validate results against historical market reactions
Update keywords, sources, and models regularly
Monitor for manipulation, coordinated shilling, and bot activity