Hybrid CNN Classification for Sentiment Analysis under Deep Learning
D. Christy Daniel1, L. Shyamala2

1Christy Daniel D.*, Research Scholar, School of Computer Science and Engineering, VIT University, Chennai, India.
2Shyamala L., Associate Professor, School of Computer Science and Engineering, VIT University, Chennai, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 27, 2020. | Manuscript published on March 10, 2020. | PP: 1473-1480 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2922039520/2020©BEIESP | DOI: 10.35940/ijitee.E2922.039520
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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 (SA) is a popular field in Natural Language Processing (NLP) which focuses on the human emotions by analyzing the lexical and syntactic features. This paper presents an efficient method to find and extract the strong emotions for the sentiment classification using the proposed hybrid Convolutional Neural Networks – Global Vectors – Complex Sentence Searching – ABstract Noun Searching (CNN-GloVe-CSS-ABNS) model. The strong emotions are mostly found in the abstract nouns than the adjectives and adverbs present in the sentences. This research aims in extracting the complex sentences with abstract nouns for the sentiment classification from the twitter data. To extract the complex sentences, the proposed Complex Sentence Searching (CSS) algorithm was used. On the other hand, another proposed algorithm named, ABstract Noun Searching (ABNS) algorithm was used for identifying the abstract nouns in the sentences based on their position in the sentences. The results of this study presents that the proposed CNN-GloVe-CSS-ABNS model outperforms the other proposed models as well as the existing models, by producing an of accuracy 94.87 per cent in sentiment classification. 
Keywords: Convolutional Neural Network, Deep Learning, Natural Language Processing, Sentiment Analysis.
Scope of the Article: Neural Information Processing