KNN classifier based approach for multi-class sentiment analysis of twitter data

  • Authors

    • Soudamini Hota Chandigarh University
    • Sudhir Pathak Chandigarh University
    2018-07-08
    https://doi.org/10.14419/ijet.v7i3.12656
  • KNN, N-Gram, SVM, Tweedy, Twitter

  • Abstract

    ‘Sentiment’ literally means ‘Emotions’. Sentiment analysis, synonymous to opinion mining, is a type of data mining that refers to the analy-sis of data obtained from microblogging sites, social media updates, online news reports, user reviews etc., in order to study the sentiments of the people towards an event, organization, product, brand, person etc. In this work, sentiment classification is done into multiple classes. The proposed methodology based on KNN classification algorithm shows an improvement over one of the existing methodologies which is based on SVM classification algorithm. The data used for analysis has been taken from Twitter, this being the most popular microblogging site. The source data has been extracted from Twitter using Python’s Tweepy. N-Gram modeling technique has been used for feature extraction and the supervised machine learning algorithm k-nearest neighbor has been used for sentiment classification. The performance of proposed and existing techniques is compared in terms of accuracy, precision and recall. It is analyzed and concluded that the proposed technique performs better in terms of all the standard evaluation parameters.

     

  • References

    1. [1] Meesala Shobha Rani, Sumathy S, “Perspectives of the performance metrics in Lexicon and Hybrid based approaches: a reviewâ€, IJET, Vol. 6, No 4, 2017.

      [2] E. Cambria, B. Schuller, Y. Xia, and C. Havasi, New Avenues in Opinion Mining and Sentiment Analysis, IEEE Intelligent Systems, 28(2), 2013, pp 15-21. https://doi.org/10.1109/MIS.2013.30.

      [3] Sasikumar.A.N, “Sentimental Analysis of Social Networking Sites for Categorization of Product Reviewsâ€, Internation Journal of Pure and Applied Mathematics, Vol. 117, 2017, pp. 87 – 92.

      [4] J. Mannar Mannan, J, Jayavel, “An adaptive sentimental analysis using ontology for retail marketâ€, IJET, Vol.7, No 1.2, 2018.

      [5] V. Uma. Ramya, K. Thirupathi Rao, “Sentiment Analysis of movie review using Machine Learning techniquesâ€, IJET, Vol.7 (2.7), 2018.

      [6] Thelwall, M., Buckley, K., & Paltoglou, G., "Sentiment strength detection for the social web", J.American Society for Information Science and Technology, Vol.63 No1, 2016, pp.163–173. https://doi.org/10.1002/asi.21662.

      [7] Paltoglou, G, & Thelwall, M. "Twitter, MySpace, Digg Unsupervised Sentiment Analysis in Social Media", ACM Transactions on Intelligent Systems and Technology, Vol.3 No 4, 2012, pp.1-19. https://doi.org/10.1145/2337542.2337551.

      [8] Wafa Zubair Al-Dyani, Adnan Hussein Yahya, Farzana Kabir Ahmad, “Challenges of Event Detection from social media streamsâ€, IJET, Vol.7 (2.15), 2018.

      [9] Socher, R., Perelygin, A., Y.Wu, J., Chuang, J., Manning, C. D., Ng, A. Y., & Potts, C., “Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebankâ€, In the Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, Washington, USA, 18-21 October 2013, Vol. 1631, pp. 1642.

      [10] Alexander Pak & Patrick Paroubek. "Twitter as a corpus for sentiment analysis and opinion mining", In Proc. LREC, Vol.10, 2010, pp.1320-1326.

      [11] Ankur Goel, Jyoti Gautam, Sitesh Kumar, “Real time sentiment analysis of tweets using Naive Bayesâ€, 2nd International Conference on Next Generation Computing Technologies (NGCT), 2016.

      [12] P. Lalitha Kumari, Ch Sathyanarayana, “A novel cluster based feature selection and document classification model on high dimension tree dataâ€, IJET, Vol.7,No1.1, 2018.

      [13] Tang, D., FuruWei, Yang, N., Zhou, M., Liu, T., & Qin, B., “Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classificationâ€, In the Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics Baltimore, Maryland, USA, June 23-25 2014, pp. 1555-1565.

      [14] Wiraj Udara Wickramaarachchi, R. K. A. R. Kariapper, “An Approach to Get Overall Emotion from Comment Text towards a Certain Image Uploaded to Social Network Using Latent Semantic Analysisâ€, 2nd International Conference on Image, Vision and Computing, 2017.

      [15] Ratna Sathappan, Tholu Sai Indira, A. Meenapriyadarsini, “Smart Recommendation System for off-the shelf medicinesâ€, IJET, Vol.6, No. 2.24, 2017.

      [16] Tharindu Weerasooriya, Nandula Perera, S.R. Liyanage, “A method to extract essential keywords from tweet using NLPâ€, 16th International Conference on Advances in ICT for Emerging Regions (ICTer), 2016.

      [17] Prasanna Moorthi N, Mathivanan V, “An improved Wrapper based feature selection for feature miningâ€, IJET, Vol. 7 (1.3), 2018.

      [18] Zhao Jianqiang, Gui Xiaolin, “Deep Convolution Neural Networks for Twitter Sentiment Analysisâ€, IEEE, 2017.

      [19] K Lavanya, C Deisy, “Twitter Sentiment Analysis Using Multi-Class SVMâ€, International Conference on Intelligent Computing and Control (I2C2'17), 2017.

      [20] Chintan Dedhia, Mrs Jyoti Ramteke, “Ensemble model for Twitter Sentiment Analysisâ€, International Conference on Inventive Systems and Control (ICISC), 2017. https://doi.org/10.1109/ICISC.2017.8068711.

      [21] Yeqing Yan, Hui Yang, Hui-ming Wang, “Two Simple and Effective Ensemble Classifiers for Twitter Sentiment Analysisâ€, Computing Conference, 2017.

      [22] Paramita Ray and Amlan Chakrabarti, “Twitter Sentiment Analysis for Product Review Using Lexicon Methodâ€, International Conference on Data Management, Analytics and Innovation (ICDMAI), 2017. https://doi.org/10.1109/ICDMAI.2017.8073512.

      [23] Ranjan Satapathy, Claudia Guerreiro, Iti Chaturvedi, Erik Cambria, “Phonetic-Based Microtext Normalization for Twitter Sentiment Analysisâ€, IEEE International Conference on Data Mining Workshops, 2017.

      [24] Shweta Rana, Archana Singh, “Comparative analysis of sentiment orientation using SVM and Naive Bayes techniquesâ€, second International Conference on Next Generation Computing Technologies (NGCT), 2016.

      [25] Pedro, J., Garcia-Laencina, Jose-Luis Sancho-Gomez, Anibal, R., Figueiras-Vidal, and Michel Verleysen, “K nearest neighbours with mutual information for simultaneous classification and missing data imputationâ€, 2009, 1483–1493.

  • Downloads

  • How to Cite

    Hota, S., & Pathak, S. (2018). KNN classifier based approach for multi-class sentiment analysis of twitter data. International Journal of Engineering & Technology, 7(3), 1372-1375. https://doi.org/10.14419/ijet.v7i3.12656

    Received date: 2018-05-10

    Accepted date: 2018-05-23

    Published date: 2018-07-08