Evaluation of Sentiment Analysis over Bilingual Cross Domain Platform using Machine Learning Approaches
S. Arun Kumar1, M. Sanjanaa Sri2, Rishendra Ravi3, Dipon Sengupta4, Arhant Chatterjee5
1S. Arun Kumar, Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India.
2Dipon Sengupta, Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India.
3Rishendra Ravi, Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India.
4M . Sanjanaa Sri, Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India.
5Arhant Chatterjee, Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 1177-1184 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2774037619/19©BEIESP
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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: Cross-Domain adaptation needs special data to get a shared characteristic with various domain. Notwithstanding, such valuable data may not generally be accessible in genuine cases. In this paper, another issue setting called Cross-Domain Sentiment Analysis in bilingual platform is addressed. It is an extraordinary instance of cross-space nostalgic examination in which diverse areas have some regular commonalities, yet in addition have their very own space explicit highlights. We influence upon normal highlights rather than beneficial data to accomplish viable adjustment. We propose a methodology, which can interface up various spaces utilizing normal highlights and at the same time decrease area divergences.
Keywords: Bilingual Analysis, Naïve Bayes Classifier, N-gram, Sentiment Analysis.
Scope of the Article: Structural Reliability Analysis