Intensity Weight Factor Based Sentiment Prediction Analysis on Tweets
Manasa K N1, M. C. PADMA2
1Manasa K. N., Asst. Professor, Department of Computer Science, St. Philomena’s College, Mysuru, Karnataka, India.
2M. C. PADMA, Professor and Head Department of Computer Science and Engineering, PES College of Engineering, Mandya, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 720-726 | Volume-8 Issue-5, January 2020. | Retrieval Number: D9590118419/2020©BEIESP | DOI: 10.35940/ijrte.D9590.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Advances in the field of sentiment analysis are quick and purposeful to explore the views or articles available on various social media platforms through the techniques of machine learning with emotions, topic analysis or polarization calculations. Although employing various machine learning techniques and emotion analysis tools, there is a direct need for modern methods. To address these challenges, the contribution of this paper involves adopting a new approach that includes emotional analysts that integrates emotional intensity and machine learning. In addition, this document also provides a comparison of sentiment analysis techniques in analyzing political views through the application of machine learning algorithms such as Naive Bayes and KNN.
Keywords: Sentiment Analyzer; WordNet; word sequence disambiguation (WSD); Twitter; machine learning; Naïve Bayes.
Scope of the Article: Machine Learning.