Dynamic Mode-Based Feature with Random Mapping for Sentiment Analysis

  • S. Sachin KumarEmail author
  • M. Anand Kumar
  • K. P. Soman
  • Prabaharan Poornachandran
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 910)


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.


Sentiment analysis Polarity identification Dynamic mode decomposition Random mapping Random kitchen sink 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • S. Sachin Kumar
    • 1
    Email author
  • M. Anand Kumar
    • 2
  • K. P. Soman
    • 1
  • Prabaharan Poornachandran
    • 3
  1. 1.Center for Computational Engineering & Networking (CEN), Amrita School of Engineering, Amrita Vishwa VidyapeethamCoimbatoreIndia
  2. 2.Department of Information TechnologyNational Institute of TechnologySurathkalIndia
  3. 3.Amrita Center for Cyber Security & Networks, Amrita Vishwa VidyapeethamAmritapuriIndia

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