Vol.68, No.2, 2021, pp.1983-1999, doi:10.32604/cmc.2021.015661
Enhancement of Sentiment Analysis Using Clause and Discourse Connectives
  • Kumari Sheeja Saraswathy, Sobha Lalitha Devi*
AU-KBC Research Centre, MIT Campus of Anna University, Chromepet, Chennai, India
* Corresponding Author: Sobha Lalitha Devi. Email:
Received 01 December 2020; Accepted 18 February 2021; Issue published 13 April 2021
The sentiment of a text depends on the clausal structure of the sentence and the connectives’ discourse arguments. In this work, the clause boundary, discourse argument, and syntactic and semantic information of the sentence are used to assign the text’s sentiment. The clause boundaries identify the span of the text, and the discourse connectives identify the arguments. Since the lexicon-based analysis of traditional sentiment analysis gives the wrong sentiment of the sentence, a deeper-level semantic analysis is required for the correct analysis of sentiments. Hence, in this study, explicit connectives in Malayalam are considered to identify the discourse arguments. A supervised method, conditional random fields, is used to identify the clause boundary and discourse arguments. For the study, 1,000 sentiment sentences from Malayalam documents were analyzed. Experimental results show that the discourse structure integration considerably improves sentiment analysis performance from the baseline system.
Natural language processing; artificial intelligence; sentiment analysis; computational linguistics; opinion mining; machine learning; information extraction; supervised learning
Cite This Article
K. Sheeja Saraswathy and S. Lalitha Devi, "Enhancement of sentiment analysis using clause and discourse connectives," Computers, Materials & Continua, vol. 68, no.2, pp. 1983–1999, 2021.
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