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Gurumoorthy, K.
- Evaluating Popular Smartphone Brands Based On Twitter Sentiment Using Textblob
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Authors
K. Gurumoorthy
1,
P. Suresh
2
Affiliations
1 Department of Computer Science, Periyar University, IN
2 Department of Computer Science, Salem Sowdeswari College, IN
1 Department of Computer Science, Periyar University, IN
2 Department of Computer Science, Salem Sowdeswari College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 3 (2022), Pagination: 2655-2660Abstract
In today’s world, social media plays a critical part in the advancement of industries, organizations, and businesses. It has been seen as a fundamental aspect that should be known to both businesses and individuals. In one way or another, everyone is associated with social media. People have been able to interact and exchange knowledge because of the mix of technology and social relationships. In the last 10 years or so, social media has become a governing medium for knowledge exchange. Sentiment Analysis (SA) allows users to express their emotions, perspectives, and opinions to the rest of the universe. Twitter is a big and quickly expanding microblogging social networking website wherein users may express themselves concisely and easily. A large number of consumer reviews for various items are emerging on Twitter. Mobile phones are a popular sector where a large number of consumer evaluations can be found. This makes it tough for a prospective consumer to read them and decide whether or not to purchase the goods. Only the precise aspects of the phones about which users have comments, as well as whether those opinions are good or negative are of importance to us. This paper proposes a solution to this problem by analyzing consumer sentiment from Twitter data to determine brand reputation based on customer happiness. In this work, Python programming is employed to perform tests on various tweets utilizing the Twitter API and for tweet pre-processing, the Natural Language Tool Kit (NLTK) package is used. The tweets dataset is then analyzed using Textblob and the intriguing results in negative, positive, and neutral emotions are displayed using various visualizations.Keywords
Mobile Phone, Net Brand Reputation (NBR), Twitter, NLTK, Textblob
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