Metoo Movement Analysis through the Lens of Social Media
P.Asha1, K. Sri Neeharika2, T. Sindhura3
1Dr. P.Asha, Asst. Prof.,Dept. of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai.
2K. Sri Neeharika, Asst. Prof.,Dept. of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai.
3T. Sindhura, Asst. Prof.,Dept. of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai.
Manuscript received on 1 August 2019. | Revised Manuscript received on8 August 2019. | Manuscript published on 30 September 2019. | PP: 1649-1651 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4432098319/19©BEIESP | DOI: 10.35940/ijrte.C4432.098319
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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: Sentiment analysis is an errand which is used to analyse people’s opinions which has been derived out of textual data seems productive for palpating various NLP applications. The grievances associated with this task is that, there prevails variety of sentiments within these documents, accompanied with diverse expressions. Therefore, it seems hard to whip out all sentiments employing a dictionary which is commonly used. This work attempts at constructing the domain sentiment dictionary, by employing the external textual data. Besides, various classification models could be utilised to classify the documents congruent to their opinion. We have also implemented topic modelling, emoticon analysis and optimized gender classification in our proposed system. Many sectors have been identified where women are being abused. Clusters are formed for these sectors and the most affected sector is also identified.
Keywords— Sentiment Analysis, Cluster, Classifier, Modelling.
Scope of the Article: Predictive Analysis