Sentimental Analysis of Olympics Tweets

Mining of this textual data or unstructured data to understand the opinion, thoughts, and emotions of a group of peoples is also known as opinion mining or sentimental analysis.From the last decade, social media data has been the main source for spreading information in various domains like entertai...

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Published inAnnals of the Romanian society for cell biology Vol. 24; no. 2; pp. 427 - 435
Main Authors Srivastava, Rajeev, Agarwal, Atul Kumar, Rana, Gunjan A
Format Journal Article
LanguageEnglish
Published Arad "Vasile Goldis" Western University Arad, Romania 01.01.2020
Subjects
Emotions
Natural language processing
Objectivity
Polarity
Politics
Sentiment analysis
Social networks
Subjectivity
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Summary:Mining of this textual data or unstructured data to understand the opinion, thoughts, and emotions of a group of peoples is also known as opinion mining or sentimental analysis.From the last decade, social media data has been the main source for spreading information in various domains like entertainment, politics, andbusiness.The main source of these social media platforms are Twitter, Facebook, and Instagram. The major sectors are business (Beier & Wagner, 2016), entertainment (Shen, Hock Chuan, & Cheng, 2016), science (Chen & Zhang, 2016), crisis management (Hiltz, Diaz, & Mark, 2011; Stieglitz, Bunker, Mirbabaie, &Ehnis, 2017a) and, politics (Stieglitz & Dang-Xuan, 2013).Social media is generating a variety of data, which can be categorized as unstructured data and structured data (Baars & Kemper, 2008).The textual content is an example of unstructured data, while the friend/follower relationship is an example of structured data. Natural Language Processing (NLP) packages of python are used to identify the polarity and subjectivity of each tweet.Polarity is used to extract positive, negative, or neutral emotions of each tweete.g.(+,-,0) from the textual data. Sentiments keep on changing, so accordingly result will also modify. 7.Conclusion The study shows that the number of positive tweets is more as compared to negative tweets related to the upcoming Olympics.
ISSN:2067-3019
2067-8282