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

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
Copyright: © 2022 |Pages: 20
DOI: 10.4018/IJIIT.296271
Article PDF Download
Open access articles are freely available for download

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.
Article Preview
Top

1. Introduction

Social media is described as “a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user generated content” (Power et al., 2011; Kaplan and Haenlein, 2010; Berthon et al., 2012; Mirzaalian & Halpenny, 2019). Examples of social media include Weblogs, file sharing sites, social networking sites, and wikis are some examples of social media (Mukherjee et al., 2021). It has now been recognised as a preferred communication medium to share and exchange information among its users (Stieglitz and Dang-Xuan, 2013; Louati et al., 2021). Users of social media use this platform frequently to express and discuss their opinions on various topics of their interest, like politics, products, weather, finance, sports, and hospitality etc. (Mirzaalian & Halpenny, 2019; Asamoah & Sharda, 2019). The popularity of social media has led to the creation of massive unstructured data in terms of blogs, posts, tweets, images, videos, messages, and reviews etc. (Mukherjee et al., 2021; Arora et al., 2021). This information is a gold mine which can be utilized for decision-making (Montoyo et al., 2012; Lee et al., 2015; Xie and Lee, 2015; Peláez et al., 2019). In certain areas, this can be even better than conventional market research because the discussion that takes place over social media is largely informal and rightly convey the mood and feelings / sentiments of the people (Yi and Liu, 2020; Alamoudi and Alghamdi, 2021; Li & Huang, 2020).

This study attempts to identify customer preferences and / or opinions about a brand from what they express on the social media platform, Twitter by using aspect level sentiment analysis. Secondly, it contributes by means of addressing the problems /limitations of supervised machine learning approach in performing sentiment analysis by creating opinion tagged corpus and finally, the aim is to recommend the best-suited machine learning classifier to perform such aspect level sentiment analysis.

The brand chosen for this study is ‘Patanjali’, which essentially deals in fast-moving consumer goods and is owned by Patanjali Ayurveda Limited, an Indian company that competes with and has shaken the likes of global multinationals like HUL (Hindustan Unilever Limited) and Colgate etc. It has forced them to redesign their product portfolio and introduce brands and products that offer a value proposition and positioning which is similar to Patanjali. The rationale behind choosing Patanjali was to decode what consumers think about and expect from a late entrant brand that has earned the status of a challenger in a market where the global giants have ruled for decades.

Complete Article List

Search this Journal:
Reset
Volume 20: 1 Issue (2024)
Volume 19: 1 Issue (2023)
Volume 18: 4 Issues (2022): 3 Released, 1 Forthcoming
Volume 17: 4 Issues (2021)
Volume 16: 4 Issues (2020)
Volume 15: 4 Issues (2019)
Volume 14: 4 Issues (2018)
Volume 13: 4 Issues (2017)
Volume 12: 4 Issues (2016)
Volume 11: 4 Issues (2015)
Volume 10: 4 Issues (2014)
Volume 9: 4 Issues (2013)
Volume 8: 4 Issues (2012)
Volume 7: 4 Issues (2011)
Volume 6: 4 Issues (2010)
Volume 5: 4 Issues (2009)
Volume 4: 4 Issues (2008)
Volume 3: 4 Issues (2007)
Volume 2: 4 Issues (2006)
Volume 1: 4 Issues (2005)
View Complete Journal Contents Listing