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Analysis of Social Media Sentiment: Users Reactions on Twitter through Machine Learning Techniques

  • Unique Paper ID: 170074
  • Volume: 11
  • Issue: 6
  • PageNo: 3099-3106
  • Abstract:
  • Sentiment analysis, or opinion mining, In recent years, social media platforms have allowed people to freely share their views resulting to public opinion turn into a mandatory subject of research - the Knowledge, Attitudes, and Practices -Social Media Strategy. Now In Twenty One Century Social Networking Vehicles Purposed for Entertainment and Business Promotion Work Together with People Created Absolute Violence and Deformation Sequence Attributing All Negative Characteristics Toward These People…in Extremal Art. This research paper investigates the semantic analysis of tweets and attempts to categorize these utterances into class, positive, negative and neutral sentiments. From its content, use and rate of adoption as well as the linguistic trends within its users, Twitter provides both a hindrance and an advantage to sentiment analysis. Our study looks at the tools and methods of sentiment detection in Twitter comments and the extent to which great meaning can be understood from such simple interactions online secondary to NLP and ML techniques being applied. Considering the semantic structures contained within Twitter posts, this paper attempts to examine the approval or disapproval of the public towards certain issues and demonstrates how this analysis can be beneficial in assessing the current attitude of people towards certain products, services, or social phenomena within the short span. What this paper also provides is a broad overview of how sentiment analysis works and the nature of the concerns, described earlier in this paper, how sentiment analysis is useful to businesses, policy makers, and researchers who want to make practical use of such analysis. Our research adds to the existing perspectives on sentiment analysis in social media by evaluating the prominent approaches and investigation challenges which are particularly relevant for the peculiar characteristics of Twitter data.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 6
  • PageNo: 3099-3106

Analysis of Social Media Sentiment: Users Reactions on Twitter through Machine Learning Techniques

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