Social Listening Through Sentiment Analysis of Twitter Data: A Case Study of Paytm IPO

Mehta Meera, Arora Shivani (), Gupta Shikha and Jhulka Arun
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Mehta Meera: Associate Professor, Department of Commerce, Shaheed Bhagat Singh College, University of Delhi, India
Arora Shivani: Professor, Department of Commerce, Shaheed Bhagat Singh College, University of Delhi, Delhi, India
Gupta Shikha: Associate Professor, Department of Commerce, Shaheed Bhagat Singh College, University of Delhi, India
Jhulka Arun: Associate Professor, Maharaja Agrasen College, Delhi University, India

SocioEconomic Challenges (SEC), 2022, vol. 6, issue 3, 39-47

Abstract: Purpose. Microblogging sites are being used by people across the globe to share their opinions and to express sentiments for everything in real time. Through social listening, companies analyse the sentiments to assess the way forward, and the researchers use it to analyse the trend or an event and give forward-looking recommendations. The objective of the paper is to analyse the sentiments of people relating to Paytm IPO which can be used to predict the way forward. Design/methodology/approach. The study attempts sentiment analysis. For this purpose, QSR NVIVO 12, the qualitative analysis tool was used to retrieve the tweets from the Twitter website. NCapture was installed for this purpose. Post data cleaning, stemming, query augmentation and classification, the Twitter data was analysed. Findings. The sentiments around the IPO of Paytm have been negative and sarcastic. The extremely negative tweets were near twice the number of extremely positive tweets. Practical implication. The study can help an investor in evaluating the investment that they might be planning in the given company. For the company, whose IPO is being considered, an analysis of the sentiments around the IPO can help in taking corrective measures, if the sentiment is negative, towards reputation building. Originality/value. The study is an original contribution to the extant literature in the field of sentiment analysis.

Keywords: social media; Twitter data; sentiment analysis; opinions; tweets (search for similar items in EconPapers)
JEL-codes: D8 G1 G12 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:socecc:v:6:y:2022:i:3:p:39-47:n:12

DOI: 10.21272/sec.63.39-47.2022

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