An enhanced hybrid feature selection technique using term frequency-inverse document frequency and support vector machine-recursive feature elimination for sentiment classification

• Main Authors: Nur Syafiqah, Mohd Nafis, Suryanti, Awang
• Format: Article
• Language: English
• Published: IEEE 2021
• Subjects: T Technology (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/31646/1/stamp.jsp_tp%3D%26arnumber%3D9387312%26tag%3D1; http://umpir.ump.edu.my/id/eprint/31646/; https://doi.org/10.1109/ACCESS.2021.3069001; https://doi.org/10.1109/ACCESS.2021.3069001
• Institution: Universiti Malaysia Pahang

Sentiment classification is increasingly used to automatically identify a positive or negative sentiment in a text review. In classification, feature selection had always been a critical and challenging problem. Most of the related feature selection for sentiment classification techniques unable to overcome problems of evaluating the significant features that will reduce the classification performance. This paper proposes an enhanced hybrid feature selection technique to improve the sentiment classification based on machine learning approaches. First, two customer review datasets namely Sentiment Labelled and large IMDB are retrieved and pre-processed. Next, the proposed feature selection technique which is the hybridization of Term Frequency-Inverse Document Frequency (TF-IDF) and Supports Vector Machine (SVM-RFE) is developed and tested on these two datasets. TF-IDF aims to measure features importance. The SVM-RFE iteratively evaluates and ranks the features. For sentiment classification, only the k-top features from the ranked features will be used. Finally, the Support Vector Machine (SVM) classifier is deployed to observe the performance of the proposed technique. The performance is measured using accuracy, precision, recall, and F-measure. The experimental results show promising performances with 84.54% to 89.56% in the measurements especially from the large IMDB dataset. The results also outperformed other related techniques in certain datasets. Consequently, the proposed technique able to reduce from 19.25% to 70.5% of the features to be classified. This reduction rate is significant in optimally utilizing the computational resources while maintaining the efficiency of the classification performance.