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A Hybrid Approach of Machine Learning and Lexicons to Sentiment Analysis: Enhanced Insights from Twitter Data of Natural Disasters

Author

Listed:
  • Shalak Mendon

    (Electronic City
    Indian Institute of Technology Bombay)

  • Pankaj Dutta

    (Indian Institute of Technology Bombay)

  • Abhishek Behl

    (Indian Institute of Technology Bombay)

  • Stefan Lessmann

    (Humboldt-Universität zu Berlin)

Abstract

The success factor of sentimental analysis lies in identifying the most occurring and relevant opinions among users relating to the particular topic. In this paper, we develop a framework to analyze users’ sentiments on Twitter on natural disasters using the data pre-processing techniques and a hybrid of machine learning, statistical modeling, and lexicon-based approach. We choose TF-IDF and K-means for sentiment classification among affinitive and hierarchical clustering. Latent Dirichlet Allocation, a pipeline of Doc2Vec and K-means used to capture themes, then perform multi-level polarity indices classification and its time series analysis. In our study, we draw insights from 243,746 tweets for Kerala’s 2018 natural disasters in India. The key findings of the study are the classification of sentiments based on similarity and polarity indices and identifying themes among the topics discussed on Twitter. We observe different sets of emotions and influencers, among others. Through this case example of Kerala floods, it shows how the government and other organizations could track the positive/negative sentiments concerning time and location; gain a better understanding of the topic of discussion trending among the public, and collaborate with crucial Twitter users/influencers to spread and figure out the gaps in the implementation of schemes in terms of design and execution. This research’s uniqueness is the streamlined and efficient combination of algorithms and techniques embedded in the framework used in achieving the above output, which can be integrated into a platform with GUI for further automation.

Suggested Citation

  • Shalak Mendon & Pankaj Dutta & Abhishek Behl & Stefan Lessmann, 2021. "A Hybrid Approach of Machine Learning and Lexicons to Sentiment Analysis: Enhanced Insights from Twitter Data of Natural Disasters," Information Systems Frontiers, Springer, vol. 23(5), pages 1145-1168, September.
    • Handle: RePEc:spr:infosf:v:23:y:2021:i:5:d:10.1007_s10796-021-10107-x
    DOI: 10.1007/s10796-021-10107-x
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    References listed on IDEAS

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    2. Abhinav Kumar & Jyoti Prakash Singh & Nripendra P. Rana & Yogesh K. Dwivedi, 2023. "Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster," Information Systems Frontiers, Springer, vol. 25(4), pages 1589-1604, August.
    3. Paras Bhatt & Naga Vemprala & Rohit Valecha & Govind Hariharan & H. Raghav Rao, 2023. "User Privacy, Surveillance and Public Health during COVID-19 – An Examination of Twitterverse," Information Systems Frontiers, Springer, vol. 25(5), pages 1667-1682, October.
    4. Peng Xie, 2022. "The Interplay Between Investor Activity on Virtual Investment Community and the Trading Dynamics: Evidence From the Bitcoin Market," Information Systems Frontiers, Springer, vol. 24(4), pages 1287-1303, August.
    5. Yuko Murayama & Hans Jochen Scholl & Dimiter Velev, 2021. "Information Technology in Disaster Risk Reduction," Information Systems Frontiers, Springer, vol. 23(5), pages 1077-1081, September.
    6. Jyoti Choudrie & Shruti Patil & Ketan Kotecha & Nikhil Matta & Ilias Pappas, 2021. "Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study," Information Systems Frontiers, Springer, vol. 23(6), pages 1431-1465, December.

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