Automated Estimation of Emotion Analysis On Social Media

Authors

  • Bushra Shaikh1  Department of information science and engineering, GSSSIETW, Mysuru, Karnataka, India
  • Chaitali Shanbhag2  Department of information science and engineering, GSSSIETW, Mysuru, Karnataka, India
  • Sheetal K3  Department of information science and engineering, GSSSIETW, Mysuru, Karnataka, India
  • Unnimaya C4  Department of information science and engineering, GSSSIETW, Mysuru, Karnataka, India
  • Mrs. Ayesha Taranum5  Department of information science and engineering, GSSSIETW, Mysuru, Karnataka, India

Keywords:

Emotion Analysis; Natural Language Processing, Social Media, Morphological Sentence Patterns, Aspect Based Approach

Abstract

Social media became popular where in people are willing to share their emotions and opinions or to participate in social networking. Accordingly, the understanding of social media usage became important. The emotion analysis is emerged as one of useful methods to analyze emotional stats expressed in textual data including social media data. However, this method still presents some limitations, particularly with based on accuracy, lexicon and aspect. To overcome and improve this weakness, we propose an automated estimation of emotion analysis in this paper by using the morphological sentence pattern model. Emotion analysis is the process of determining whether a opinion of writing is positive or negative.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-05-08

Issue

Volume 4, Issue 6, May-June-2018

Section

Research Articles

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Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Bushra Shaikh1, Chaitali Shanbhag2, Sheetal K3, Unnimaya C4, Mrs. Ayesha Taranum5, " Automated Estimation of Emotion Analysis On Social Media , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 6, pp.532-537, May-June-2018.