Abstract
Large amounts of data on the web can provide valuable information. For example, product reviews help business owners measure customer satisfaction. Sentiment analysis classifies texts into two polarities: positive and negative. This paper examines movie reviews and tweets using a new term weighting scheme, called one-over-sigma (1/sigma), on benchmark datasets for sentiment classification. The proposed method aims to improve the performance of sentiment classification. The results show that 1/sigma is more accurate than the popular term weighting schemes. In order to verify the conclusion in [1] that entropy reflects the discriminating power of terms, we also report a comparison of entropy values for different term weighting schemes.
| Original language | English |
|---|---|
| Journal | International Journal of Computer and Information Engineering |
| Volume | 15 |
| Issue number | 7 |
| State | Published - 2021 |