03. Natural Language Processing (NLP) for Narrative Detection

Objective:


Once data has been collected, Neos will analyze the tweets to identify emerging narratives or trends relevant to crypto projects.

Implementation:

• Preprocessing: The first step will involve cleaning the data by removing irrelevant content such as links, mentions, special characters, and stop words. Tokenization will then break down tweets into individual words for easier analysis.

• Topic Modeling: Neos will employ topic modeling algorithms like Latent Dirichlet Allocation (LDA) to uncover underlying themes or topics in the tweets. This will help identify common discussions about crypto-related technologies, projects, and events.

• Sentiment Analysis: Advanced machine learning models will be used for sentiment analysis, classifying tweets as positive, neutral, or negative. This will aid in detecting emerging narratives with favorable or unfavorable tones.

• Named Entity Recognition (NER): Neos will use NER to identify specific cryptocurrencies, blockchain technologies, and key influencers mentioned in tweets. This helps focus on high-impact mentions that drive trends.


• Trend Detection: By tracking the frequency and context of certain keywords, hashtags, and sentiments over time, Neos will detect when a narrative is gaining traction. Real-time monitoring will identify emerging trends before they fully take shape.