Effect of Negation in Sentences on Sentiment Analysis and Polarity Detection

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Sentiment analysis is one of the sub-domains of Natural Language Processing (NLP) that is of piqued interest in the research community. With the advent of e-commerce and social media, more and more customer opinions are being provided online in the written text form. Nowadays, sentiment analysis provides a way for companies to understand customer opinions towards products and services in a global marketplace. Negative sentences or using negations in sentences have a significant impact on sentiment polarity detection. Inappropriate processing of negations in leads to biases and misclassification of sentiments. In this paper, we provide a novel end-to-end sentiment analysis approach to handle negations, along with the inclusion of negation identification and negation scope marking. Our approach introduces a customized negation marking algorithm for explicit negation detection and perform experiments on sentiment analysis with different machine learning algorithms such as Naïve Bayes, Support Vector Machines, Artificial Neural Network (ANN), and Recurrent Neural Network (RNN) on sentiment analysis of Amazon reviews, specifically of cell phones. By evaluating the effect of the negation algorithm on the sentiment analysis tasks, the RNN achieved the best accuracy of 95.67% when combined with our negation marking processing, exceeding its accuracy without any identification of negative sentences. Further, our approach was applied to another dataset of Amazon reviews and demonstrated a significant improvement in the overall accuracy.

Original languageEnglish (US)
Pages (from-to)370-379
Number of pages10
JournalProcedia Computer Science
Volume185
DOIs
StatePublished - 2021
Event2021 Complex Adaptive Systems Conference - Malvern, United States
Duration: Jun 16 2021Jun 18 2021

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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