Decoding Customer Opinion for Products or Brands Using Social Media Analytics: A Case Study on Indian Brand Patanjali

Madan Lal Yadav, Anurag Dugar, Kuldeep Baishya

Source Title: International Journal of Intelligent Information Technologies (IJIIT)18(2)

ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781799893837|DOI: 10.4018/IJIIT.296271

MLA

Yadav, Madan Lal, et al. "Decoding Customer Opinion for Products or Brands Using Social Media Analytics: A Case Study on Indian Brand Patanjali." IJIIT vol.18, no.2 2022: pp.1-20. http://doi.org/10.4018/IJIIT.296271

APA

Yadav, M. L., Dugar, A., & Baishya, K. (2022). Decoding Customer Opinion for Products or Brands Using Social Media Analytics: A Case Study on Indian Brand Patanjali. International Journal of Intelligent Information Technologies (IJIIT), 18(2), 1-20. http://doi.org/10.4018/IJIIT.296271

Chicago

Yadav, Madan Lal, Anurag Dugar, and Kuldeep Baishya. "Decoding Customer Opinion for Products or Brands Using Social Media Analytics: A Case Study on Indian Brand Patanjali," International Journal of Intelligent Information Technologies (IJIIT) 18, no.2: 1-20. http://doi.org/10.4018/IJIIT.296271

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Abstract

This study uses aspect-level sentiment analysis using lexicon-based approach to analyse online reviews of an Indian brand called Patanjali, which sells many FMCG products under its name. These reviews have been collected from the microblogging site Twitter from where a total of 4961 tweets about 10 Patanjali branded products have been extracted and analysed. Along with the aspect-level sentiment analysis, an opinion-tagged corpora has also been developed. Machine learning approaches—support vector machine (SVM), decision tree, and naïve bayes—have also been used to perform the sentiment analysis and to figure out the appropriate classifiers suitable for such product reviews analysis. The authors first identify customer preferences and/or opinions about a product or brand by analyisng online customer reviews as they express them on the social media platform Twitter by using aspect-level sentiment analysis. The authors also address the limitations of scarcity of opinion tagged data required to train supervised classifiers to perform sentiment analysis by developing tagged corpora.