Semi-Supervised Non-Linear Dimensionality Reduction Technique for Sentiment Analysis Classification
M. Anandapriya1, M. S. Gowtham2, Kamalraj Subramaniam3
1M. Anandapriya, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India.
2M. S. Gowtham, Department of Electronics and Communication Engineering, Karpagam Institute of Technology, Coimbatore, India.
3Dr. Kamalraj Subramaniam, Department of Electronics and Communication Engineering, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, India.
Manuscript received on 23 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1721-1725 | Volume-8 Issue-9, July 2019 | Retrieval Number: I7793078919/19©BEIESP | DOI: 10.35940/ijitee.I7793.078919
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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: With the quick development in data advances, client created substance, for example, reviews, ratings, recommendations can be advantageously posted on the web, which have powered enthusiasm for sentiment classification. The quantity of records accessible on both online and offline is expanding drastically. Sentiment Classification has a wide scope of utilizations in review related sites. In this paper, we present our investigations about some exploration paper in this field and exhibited our plan to distinguish the sentiment extremity of a given content as positive or negative by lessening the documents dimension, through utilizing semi-supervised non-linear dimensionality decrease technique. For Sentiment Classification, Random Subspace strategy is utilized. For exploratory assessment, openly accessible sentiment datasets can be utilized to check the adequacy of the proposed technique.
Index Terms: Sentiment Classification, Random Subspace, Laplacian Eigen Map, Semi-supervised Non-Linear Dimensionality Reduction
Scope of the Article: Classification