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Dynamic mode-based feature with random mapping for sentiment analysis - 2020

Dynamic Mode-Based Feature With Random Mapping For Sentiment Analysis

Research Area:  Machine Learning


Sentiment analysis (SA) or polarity identification is 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.


Author(s) Name:   S. Sachin KumarEmail authorM. Anand KumarK. P. SomanPrabaharan Poornachandran

Journal name:  Intelligent Systems, Technologies and Applications

Conferrence name:  

Publisher name:  SPRINGER

DOI:  10.1007/978-981-13-6095-4_1

Volume Information:  pp 1-15