A scheme of pairwise feature combinations to improve sentiment classification using book review dataset

• Main Authors: Abubakar, Haisal Dauda, Huspi, Sharin Hazlin, Mahmood Umar, Mahmood Umar
• Format: Article
• Language: English
• Published: Computer Science and Information System 2022
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/108822/1/SharinHazlinHuspi2022_ASchemeofPairwiseFeatureCombinations.pdf; http://eprints.utm.my/108822/; http://dx.doi.org/10.11113/ijic.v12n1.344

Sentiment Analysis is a Natural Language Processing (NLP) domain related to the identification or extraction of user sentiments or opinions from written language. Although the approaches to achieve the goals may vary, Machine Learning (ML) methods are gradually becoming the preferred method because of their ability to automatically draw useful insight from data regardless of their complexity. However, an important prerequisite for most ML algorithms to learn from text data is to encode them into numerical vectors. Popular approaches to this include word level representation methods TF-IDF, distributed word representations (word2vec) and distributed document representations (doc2vec). Each of these methods has demonstrated remarkable success in representing the encoded text, however we found that no method has been set to be excellence in all tasks. Motivated by this challenge, an improved scheme of pairwise fusion are proposed for sentiment classification of book reviews. In the experimental findings, Artificial Neural Networks (ANN) and Logistic Regression (LR) classifiers showed that the proposed scheme improved the performance compared to the single method vectorization method. We see that TF-IDF-word2vec performed best among other methods with a mean accuracy of 91.0% (ANN) and 92.5% (LR); showed an improvement of 0.7% and 0.2% respectively over TF-IDF which is the best single vector method. Thus, the proposed method can used as a compact alternative to the popular bag-of-n-gram models as it captures contextual information of encoded document with a less sparse data.