Socio-Analyzer: A Sentiment Analysis Using Social Media Data

7 pages•Published: September 26, 2019

Abstract

The usage of social media is rapidly increasing day by day. The impact of societal changes is bending towards the peoples’ opinions shared on social media. Twitter has re- ceived much attention because of its real-time nature. We investigate recent social changes in MeToo movement by developing Socio-Analyzer. We used our four-phase approach to implement Socio-Analyzer. A total of 393,869 static and stream data is collected from the data world website and analyzed using a classifier. The classifier identify and categorize the data into three categories (positive, neutral, and negative). Our results showed that the maximum peoples’ opinion is neutral. The next higher number of peoples’ opinion is contrary and compared the results with TextBlob. We validate the 765 tweets of weather data and generalize the results to MeToo data. The precision values of Socio-Analyzer and TextBlob are 70.74% and 72.92%, respectively, when considered neutral tweets as positive.

Keyphrases: data classification, machine learning, sentiment analysis

In: Frederick Harris, Sergiu Dascalu, Sharad Sharma and Rui Wu (editors). Proceedings of 28th International Conference on Software Engineering and Data Engineering, vol 64, pages 61-67.

Links:	https://easychair.org/publications/paper/HJnm
	https://doi.org/10.29007/kzk1

BibTeX entry

@inproceedings{SEDE2019:Socio_Analyzer_Sentiment_Analysis,
  author    = {Ajay Bandi and Aziz Fellah},
  title     = {Socio-Analyzer: A Sentiment Analysis Using Social Media Data},
  booktitle = {Proceedings of 28th International Conference on Software Engineering and Data Engineering},
  editor    = {Frederick Harris and Sergiu Dascalu and Sharad Sharma and Rui Wu},
  series    = {EPiC Series in Computing},
  volume    = {64},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/HJnm},
  doi       = {10.29007/kzk1},
  pages     = {61-67},
  year      = {2019}}

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