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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)

Abstract

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.

Keywords

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

References

  1. 1.
    Zikopoulos, P., Eaton, C., DeRoos, D., Deutch, T., Lapis, G.: Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Osborne Media (2011)Google Scholar
  2. 2.
    Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of twitter data. In: Proceedings of ACL 2011 Workshop on Languages in Social Media, pp. 30–38 (2011)Google Scholar
  3. 3.
    Saif, H., He, Y., Alani, H.: Semantic smoothing for twitter sentiment analysis. In: Proceeding of the 10th International Semantic Web Conference (ISWC) (2011)Google Scholar
  4. 4.
    Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal texts. J. Artif. Intell. Res. 723–762 (2014)CrossRefGoogle Scholar
  5. 5.
    Pandey, P., Govilkar, S.: A framework for sentiment analysis in Hindi using HSWN. Int. J. Comput. Appl. 119(19) (2015)CrossRefGoogle Scholar
  6. 6.
    Mittal, N., Agarwal, B., Chouhan, G., Bania, N., Pareek, P.: Sentiment analysis of Hindi review based on negation and discourse relation. In: International Joint Conference on Natural Language Processing, Nagoya, Japan (2013)Google Scholar
  7. 7.
    Joshi, A., Balamurali, A.R., Bhattacharyya, P.: A fall-back strategy for sentiment analysis in Hindi: a case study. In: Proceedings of the 8th ICON (2010)Google Scholar
  8. 8.
    Das, A., Bandyopadhyay, S.: SentiWordNet for Indian Languages, Asian Federation for Natural Language Processing (COLING), China, pp. 56–63 (2010)Google Scholar
  9. 9.
    Gupta, S.K., Ansari, G.: Sentiment analysis in Hindi Language: a survey. Int. J. Mod. Trends Eng. Res. (2014)Google Scholar
  10. 10.
    Sharma, R., Nigam, S., Jain, R.: Opinion mining in Hindi Language: a survey. Int. J. Found. Comput. Sci. Technol. (IJFCST) 4(2) (2014)CrossRefGoogle Scholar
  11. 11.
    Pooja, P., Sharvari, G.: A survey of sentiment classification techniques used for Indian regional languages. Int. J. Comput. Sci. Appl. (IJCSA) 5(2), 13–26 (2015)Google Scholar
  12. 12.
    Arunselvan, S.J., Anand Kumar, M., Soman, K.P.: Sentiment analysis of Tamil movie reviews via feature frequency count. In: International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS 15). IEEE (2015)Google Scholar
  13. 13.
    Neethu, M., Nair, J.P.S., Govindaru, V.: Domain specific sentence level mood extraction from Malayalam text. In: Advances in Computing and Communications (ICACC), pp. 78–81 (2012)Google Scholar
  14. 14.
    Sachin Kumar, S., Anand Kumar, M., Soman, K.P.: Sentiment Analysis on Malayalam Twitter data using LSTM and CNN. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9468, pp. 320–334. Springer, Hyderabad India (2017)Google Scholar
  15. 15.
    Sachin Kumar, S., Premjith, B., Anand Kumar, M., Soman, K.P.: AMRITA\_CEN-NLP@SAIL2015: Sentiment Analysis in Indian Language Using Regularized Least Square Approach with Randomized Feature Learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9468, pp. 671–683. Springer, Hyderabad India (2015)Google Scholar
  16. 16.
    Patra, B.G., Das, D., Das, A., Prasath, R.: Shared task in sentiment analysis in Indian Languages (SAIL) tweets—an overview. In: The Proceedings of Mining Intelligence and Knowledge Exploration. Springer, ISBN: 978-3-319-26832-3Google Scholar
  17. 17.
    Sachin Kumar, S., Anand Kumar, M., Soman, K.P.: Deep learning based part-of-speech tagging for Malayalam Twitter data (Special issue: deep learning techniques for natural language processing). J. Intell, Syst (2018)Google Scholar
  18. 18.
    Sachin Kumar, S., Anand Kumar, M., Soman, K.P.: Identifying Sentiment of Malayalam Tweets using Deep learning, Digital Business, pp.391-408. Springer, Cham (2019)Google Scholar
  19. 19.
    Schmid, P.J.: Dynamic mode decomposition of numerical and experimental data. J. Fluid Mech. 656, 5–28 (2010)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Rahimi, A., Recht, B.: Random features for large-scale kernel machines. In: Advances in Neural Information Processing Systems, pp. 1177–1184 (2008)Google Scholar
  21. 21.
    Sarkar, K., Chakraborty, S.: A sentiment analysis system for Indian language tweets. In: International Conference on Mining Intelligence and Knowledge Exploration, pp. 694-702. Springer, Cham (2015)CrossRefGoogle Scholar
  22. 22.
    Kumar, A., Kohail, S., Ekbal, A., Biemann, C.: IIT-TUDA: system for sentiment analysis in indian languages using lexical acquisition. In: International Conference on Mining Intelligence and Knowledge Exploration, pp. 684–693. Springer, Cham (2015)CrossRefGoogle Scholar
  23. 23.
    Prasad, S.S., Kumar, J., Prabhakar, D.K., Pal, S.: Sentiment classification: an approach for Indian language tweets using decision tree. In: International Conference on Mining Intelligence and Knowledge Exploration, pp. 656–663. Springer, Cham (2015)CrossRefGoogle Scholar
  24. 24.
    Se, S., Vinayakumar, R., Anand Kumar, M., Soman, K.P.: AMRITA-CEN@ SAIL2015: sentiment analysis in Indian languages. In: International Conference on Mining Intelligence and Knowledge Exploration, pp. 703–710. Springer, Cham (2015)CrossRefGoogle Scholar
  25. 25.
    Sarkar, K., Bhowmick, M.: Sentiment polarity detection in bengali tweets using multinomial Nave Bayes and support vector machines. In: 2017 IEEE Calcutta Conference (CALCON), pp. 31–36. IEEE (2017)Google Scholar
  26. 26.
    Phani, S., Lahiri, S., Biswas, A.: Sentiment analysis of Tweets in three Indian Languages. In: Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP2016), pp. 93–102 (2016)Google Scholar

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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