Building Large Scale Cloud System for Product Sentiment Analysis using Genetic Algorithm Based Feature Selection
P Vasudevan1, K P Kaliyamurthie2
1P Vasudevan, Research Scholar, Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Chennai, (Tamil Nadu), India.
2K P Kaliyamurthie, Professor & Dean, Department of CSE, Bharath Institute of Higher Education and Research, Chennai, (Tamil Nadu), India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 743-748 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2745037619/19©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: In Sentiment analysis, any data driven approach involves changing a piece of text into a feature vector. An optimization scheme of the best-first search which decreases the amount of memory required is referred to as beam search. The possibility of the Beam Search finding the goal can be improvised using a more precise heuristic function as well as a greater beam width. This work covers the local beam search based on feature selection and Genetic Algorithm (GA). A subset of features can be found utilizing the GA where, the bits of chromosomes indicate the presence or the absence of features. Also, for obtaining the best sub-optimal set, the global maximum for the objective function can be discovered. Here, the performance of the predictor is the objective function. As the performance of Support Vector Machine (SVM) in real-world applications is relatively greater than in case of pattern classification, this has been widely investigated in case of machine learning.
Keywords: Sentiment Analysis, local beam search, Genetic Algorithm (GA) and Support Vector Machine (SVM).
Scope of the Article: Building Energy