TF-TDA: A Novel Supervised Term Weighting Scheme for Sentiment Analysis

Abstract

In text classification tasks, such as sentiment analysis (SA), feature representation and weighting schemes play a crucial role in classification performance. Traditional term weighting schemes depend on the term frequency within the entire document collection; therefore, they are called unsupervised term weighting (UTW) schemes. One of the most popular UTW schemes is term frequency–inverse document frequency (TF-IDF); however, this is not sufficient for SA tasks. Newer weighting schemes have been developed to take advantage of the membership of documents in their categories. These are called supervised term weighting (STW) schemes; however, most of them weigh the extracted features without considering the characteristics of some noisy features and data imbalances. Therefore, in this study, a novel STW approach was proposed, known as term frequency–term discrimination ability (TF-TDA). TF-TDA mainly presents the extracted features with different degrees of discrimination by categorizing them into several groups. Subsequently, each group is weighted based on its contribution. The proposed method was examined over four SA datasets using naive Bayes (NB) and support vector machine (SVM) models. The experimental results proved the superiority of TF-TDA over two baseline term weighting approaches, with improvements ranging from 0.52% to 3.99% in the F1 score. The statistical test results verified the significant improvement obtained by TF-TDA in most cases, where the p-value ranged from 0.0000597 to 0.0455.

The Effect of Feature Weighting on Sentiment Analysis TikTok Application Using The RNN Classification

“…The results of the study [12] show that the accuracy of the TF IDF weighting in Sentiment Analysis of the New State Capital of Indonesia is 88,8%. In comparison, research [13] proves that the TF IDF method still has better accuracy results than TF RF. While research [10], which used the Word2Vec feature extraction in the RNN classification, produced significant numbers in increasing the model's accuracy.…”