Title: Sentiment classification of review data using sentence significance score optimisation

Authors: Ketan Kumar Todi; S.N. Muralikrishna; B. Ashwath Rao

Addresses: Department of CSE, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka-576104, India ' Department of CSE, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka-576104, India ' Department of CSE, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka-576104, India

Abstract: A significant amount of work has been done in the field of sentiment analysis in textual data using the concepts and techniques of natural language processing (NLP). In this work, unlike the existing techniques, we present a novel method wherein we consider the significance of the sentences in formulating the opinion. Often in any review, the sentences in the review may correspond to different aspects which are often irrelevant in deciding whether the sentiment is positive or negative on a topic. Thus, we assign a sentence significance score to evaluate the overall sentiment of the review. We employ a clustering mechanism followed by the neural network approach to determine the optimal significance score for the review. The proposed supervised method shows a higher accuracy than the state-of-the-art techniques. We further determine the subjectivity of sentences and establish a relationship between subjectivity of sentences and the significance score. We experimentally show that the significance scores found in the proposed method correspond to identifying the subjective sentences and objective sentences in reviews. The sentences with low significance score corresponds to objective sentences and the sentences with high significance score corresponds to subjective sentences.

Keywords: aspect; sentiment classification; clustering; neural network; optimisation; significance score.

DOI: 10.1504/IJDATS.2021.114670

International Journal of Data Analysis Techniques and Strategies, 2021 Vol.13 No.1/2, pp.59 - 71

Received: 18 Oct 2018
Accepted: 03 May 2019

Published online: 23 Apr 2021 *

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