Sentiment-Based Rating Model using VADER and TextBlob
Raj Aryan, Karan Gupta , Anjaneya Gupta, Ashwin Tiwari, Suteerth Subramaniam
Sentiment Analysis, Natural Language Processing, VADER, TextBlob, Scaling Methods, Rating Systems, Model Evaluation, Machine Learning, Sentiment Polarity, Text Analysis, Data Normalization, Predictive Accuracy.
This study presents an innovative sentiment-based rating model designed to convert sentiment analysis outputs into a standardized 5-star rating system using various scaling methods. Utilizing VADER and TextBlob for comprehensive sentiment analysis, our model employs linear, exponential, piece-wise linear, quantile, and sigmoid scaling methods to normalize sentiment scores effectively. We systematically evaluate the effectiveness of each scaling method, aiming to optimize both the predictive accuracy and reliability of our rating conversions. The performance metrics, including accuracy, Mean Squared Error (MSE), and F1 scores, demonstrate the strengths and limitations of each method, providing insights into their suitability for diverse analytical needs in sentiment-based applications.
Article Details
Unique Paper ID: 164257

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 774 - 785
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