Overview
This sentiment analysis tool evaluates the emotional tone of X's posts. It combines machine learning models and natural language processing (NLP) techniques to assess whether a text's sentiment is positive, negative, or neutral.
How It Works
- Text Preprocessing
- Tokenization: Breaks text into individual words
- Case Normalization: Converts all text to lowercase
- Stop Word Removal: Eliminates common words like "and", "the"
- Lemmatization: Reduces words to their base form
- Sentiment Analysis Models
- Logistic Regression: Uses TF-IDF vectorization
- LSTM: Processes sequences of data
- VADER: Lexicon and rule-based tool for social media
- Combining Model Outputs
- The outputs from Logistic Regression, LSTM, and VADER are combined using weighted averages to get a nuanced understanding of the sentiment. For example, more weight might be given to VADER for social media text as it's specifically designed for such content.
Scoring System
- The final sentiment score is a composite measure derived from the individual scores of the Logistic Regression, LSTM, and VADER models.
- Thresholding: Scores are thresholded to classify them into categories like "very positive", "positive", "neutral", "negative", and "very negative". Customizable thresholds allow fine-tuning based on specific needs or contexts.
Why Is This Useful?
- Business Insights: Tailoring products and marketing strategies
- Social Media Monitoring: Proactive brand reputation management
- Market Research: Insights into public opinion