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Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis

Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis

Shailendra Kumar Singh, Manoj Kumar Sachan
Copyright: © 2022 |Pages: 23
ISBN13: 9781668463031|ISBN10: 1668463032|EISBN13: 9781668463048
DOI: 10.4018/978-1-6684-6303-1.ch033
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MLA

Singh, Shailendra Kumar, and Manoj Kumar Sachan. "Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis." Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, edited by Information Resources Management Association, IGI Global, 2022, pp. 596-618. https://doi.org/10.4018/978-1-6684-6303-1.ch033

APA

Singh, S. K. & Sachan, M. K. (2022). Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis. In I. Management Association (Ed.), Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines (pp. 596-618). IGI Global. https://doi.org/10.4018/978-1-6684-6303-1.ch033

Chicago

Singh, Shailendra Kumar, and Manoj Kumar Sachan. "Classification of Code-Mixed Bilingual Phonetic Text Using Sentiment Analysis." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, edited by Information Resources Management Association, 596-618. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-6303-1.ch033

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Abstract

The rapid growth of internet facilities has increased the comments, posts, blogs, feedback, etc., on a large scale on social networking sites. These social media data are available in an unstructured form, which includes images, text, and videos. The processing of these data is difficult, but some sentiment analysis, information retrieval, and recommender systems are used to process these unstructured data. To extract the opinion and sentiment of internet users from their written social media text, a sentiment analysis system is required to develop, which can work on both monolingual and bilingual phonetic text. Therefore, a sentiment analysis (SA) system is developed, which performs well on different domain datasets. The system performance is tested on four different datasets and achieved better accuracy of 3% on social media datasets, 1.5% on movie reviews, 1.35% on Amazon product reviews, and 4.56% on large Amazon product reviews than the state-of-art techniques. Also, the stemmer (StemVerb) for verbs of the English language is proposed, which improves the SA system's performance.

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