Discriminant Variance Criterion for Sentiment Analysis
Lingbin Jin, Li Zhang · 2022 26th International Conference on Pattern Recognition (ICPR) · 2022
The high dimensionality and sparsity of text data are major obstacles for sentiment analysis. These obstacles may be removed by using some technology such as feature selection. In this paper, we propose a filter feature selection method, called Discriminant Variance Criterion (DVC), for sentiment analysis. DVC utilizes feature and class variances to select discriminative features. Statistically, the feature variance of a feature is its total scatter. Apart from it, DVC also takes into account the class variance of a feature in each class, i.e., class scatter, by introducing the class information of data. As a result, DVC selects terms with a higher total scatter and a lower class scatter as the optimal feature subset. The effectiveness and efficiency of DVC is demonstrated by experimental results on five sentiment datasets using three widely used classifiers including Support Vector Machine (SVM), Multinomial Naive Bayes (MNB) and Logistic Regression (LR).