Classification of user’s review using modified logistic regression technique

Raghavendra Reddy () and U. M. Ashwin Kumar
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Raghavendra Reddy: REVA University
U. M. Ashwin Kumar: REVA University

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 1, No 25, 279-286

Abstract: Abstract In recent years, classification and analysis of user reviews or opinions are becoming one of the significant aspects of sentiment analysis. It involves finding the polarity of each review created by the user on social networking through opinion mining. The three review polarity indicators are positive, negative and neutral. User’s sentiments are expressed in specific emotions, numbers, ratings and words for classification. Existing research work lacks accurate results due to the high ambiguity of review classification and analysis in interpreting the overall polarity, thereby proposing a modified logistic regression technique to solve such problems used for sentiment analysis and text processing. The proposed technique involves support count estimation and classification of reviews. It considers multiple independent words having similar meanings in parallel. The movie review dataset is regarded as a reliable source. The performance parameters in the proposed technique outperform the conventional methods by 90%, 78.6%, 75.6% and 76.5% concerning classification accuracy, precision, recall, and f-measure, respectively.

Keywords: Movie reviews; Machine learning; Opinion mining; Sentiment analysis and modified logistic regression (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-022-01711-4

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