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Dynamic Mode-Based Feature with Random Mapping for Sentiment Analysis

Publication Type : Conference Paper

Publisher : Springer Singapore

Source : Intelligent Systems, Technologies and Applications, Springer Singapore, Singapore (2020)

ISBN : 9789811360954

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Electronics and Communication

Year : 2020

Abstract : Sentiment analysis (SA) orSachin Kumar, S. polarityAnand Kumar, M. identificationSoman, K. P. isPoornachandran, Prabaharan a research topic which receives considerable number of attention. The work in this research attempts to explore the sentiments or opinions in text data related to any event, politics, movies, product reviews, sports, etc. The present article discusses the use of dynamic modes from dynamic mode decomposition (DMD) method with random mapping for sentiment classification. Random mapping is performed using random kitchen sink (RKS) method. The present work aims to explore the use of dynamic modes as the feature for sentiment classification task. In order to conduct the experiment and analysis, the dataset used consists of tweets from SAIL 2015 shared task (tweets in Tamil, Bengali, Hindi) and Malayalam languages. The dataset for Malayalam is prepared by us for the work. The evaluations are performed using accuracy, F1-score, recall, and precision. It is observed from the evaluations that the proposed approach provides competing result.

Cite this Research Publication : S. S. Kumar, M. Kumar, A., Dr. Soman K. P., and Poornachandran, P., “Dynamic Mode-Based Feature with Random Mapping for Sentiment Analysis”, in Intelligent Systems, Technologies and Applications, Singapore, 2020.

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