Movie Sentiment Analysis using Feature Dictionary and Multiview Light Semi Supervised Convolution Neural Network
Chaitra Kulkarni1, R Suchithra2

1Chaitra Kulkarni*, Jain University, Bangalore, India.
2Dr. R Suchithra, Jain University, Bangalore, India
Manuscript received on September 20, 2020. | Revised Manuscript received on October 10, 2020. | Manuscript published on October 10, 2020. | PP: 263-271 | Volume-9 Issue-12, October 2020 | Retrieval Number: 100.1/ijitee.L79811091220 | DOI: 10.35940/ijitee.L7981.1091220
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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: Emotional information in film commentary is very important for emotional analysis. An emotional analysis that focuses on classifying opinions into positive and negative classes according to an emotional glossary is a study. Most existing research focuses on word synthesis and user evaluation, while users’ attitudes toward feedback are ignored. To consider this point, this paper uses an emotional analysis and in-depth learning approach to examine the relationship between online film reviews, and this point is used for movie box revenue efficiency. In this paper, this work present a 11 different types of Feature Dictionary. It is modeled with information from sentences (i.e., reviews) and aspects simultaneously. First, Feature Dictionary is created with all aspects of the sentence. After obtaining the aspects, it utilize all data in the source domain and the target domain for training Multiview Light Semi Supervised Convolution Neural Network (MLSSCNN) classifier. To understand the predictive performance of this approach several performance metrics are used. The experimental result shows that the MLSSCNN provides a superior predictive effect than other classifier. 
Keywords: Natural Language Processing (NLP), Emotion Recognition, social media analysis, Sentiment Analysis, Box office Prediction, Twitter, Feature Dictionary, Aspect Ratio and MLSSCNN.