Credibility Adjusted Term Frequency: A Supervised Term Weighting Scheme for Sentiment Analysis and Text Classification

TL;DR

This paper introduces a supervised term weighting scheme that adjusts tf-idf for improved sentiment analysis and text classification, demonstrating superior performance over baseline methods across various benchmarks.

Contribution

The paper proposes a novel supervised weighting scheme for term frequency adjustment in tf-idf, enhancing classification accuracy in sentiment analysis and text classification tasks.

Findings

Outperforms baseline weighting schemes on multiple benchmarks.

Effective on both snippets and longer documents.

Robust across different datasets.

Abstract

We provide a simple but novel supervised weighting scheme for adjusting term frequency in tf-idf for sentiment analysis and text classification. We compare our method to baseline weighting schemes and find that it outperforms them on multiple benchmarks. The method is robust and works well on both snippets and longer documents.