Journal of Information and Knowledge (Formerly SRELS Journal of Information Management)

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A Comprehensive Classification of Sentiment Reviews of Twitter Data in the Domain of Climatology using Machine Learning Techniques


Affiliations
1 Department of Library and Information Science, Jadavpur University, Kolkata − 700032, West Bengal, India
     

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Purpose: This study aims at classification of sentiment reviews of Twitter data in the domain of climatology using machine learning techniques. It focuses on the text classification in order to determine the people’s intension about the climatic issues i.e., climate change, climate variability, environmental aspects etc. This paper portrays a comparison of results obtained by applying different classification algorithms like Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbour (KNN), Decision Tree Classifier, Neural Network classifier etc. These algorithms are used to classify a sentimental review and people’s emotions associated with climate. Design/Methodology/Approach: Total 2265 climate reviews data have been taken from Twitter’s developers’ account. After that, we pre-processed the total dataset by removing various symbols, HTTP tags, punctuation, etc. The pre-processed text were analysed and represented through Topic modelling, Multi Dimensional Scaling (MDS) and also Visualization of Heatmap. Next, bag of words are evaluated through various algorithms such as Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbour (KNN), Decision Tree Classifier and Neural Network classifier. After applying above mentioned classifier, datasets are tested and scores are noted. For the experiment, 70 % of total reviews (i.e.1586) are used for model training and 30% of total reviews (i.e. 680) are used for testing the models. Findings: By performing different algorithms, it shows that Random Forest classifier algorithm works well than other mentioned classifiers and most of the people have positive sentiment towards climate according to Valence Aware Dictionary for Sentiment Reasoning (VADER).

Keywords

Algorithms Classifier Techniques, Machine Learning Techniques, Polarity, Opinion Mining, Reviews, Sentiment Analysis.
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About The Authors

Apala Chatterjee
Department of Library and Information Science, Jadavpur University, Kolkata − 700032, West Bengal
India

Shampa Mahato
Department of Library and Information Science, Jadavpur University, Kolkata − 700032, West Bengal
India

Sunil Kumar Chatterjee
Department of Library and Information Science, Jadavpur University, Kolkata − 700032, West Bengal
India


Notifications

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  • Shaver, P., Schwartz, J., Kirson, D. and O’connor, C. (1987). Emotion knowledge: Further exploration of a prototype approach. Journal of personality and social psychology, 52(6), 1061. https://doi.org/10.1037/0022-3514.52.6.1061. PMid:3598857.

  • Ekman, P.; Friesen, W. V. and Ellsworth, P. (1972). Emotion in the Human Face= Guidelines for Research and Integrational of Findings.

  • Tripathy, A. (2015). Classification of sentimental reviews using machine learning techniques. Procedia Computer Science, 57, 821-829. https://doi.org/10.1016/j.procs.2015.07.523.

  • Medhat, W. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113. https://doi.org/10.1016/j.asej.2014.04.011.

  • Thelwall, M. (2011). Sentiment in twitter events. Journal of American Society for Information Science and Technology, 62(2), 406-418. https://doi.org/10.1002/asi.21462.

  • Dey, L. (2009). Opinion Mining from Noisy Text Data, AND ‘o8: Proceedings of the Second Work Hop on Analytics for Noisy Unstructured Text Data; p. 83-90. https://doi.org/10.1145/1390749.1390763.

  • Zhou, L. (2008). Ontology-supported polarity mining. Journal of the American Society for Information Science and Technology, 59(1), 98-110. https://doi.org/10.1002/asi.20735.

  • Wilson, T., Wiebe, J. and Hoffmann, P. (2005). Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. In: Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing; p. 347-354. https://doi.org/10.3115/1220575.1220619. PMCid:PMC3320443.

  • Turney, P. D. (2002). Thumbs up or Thumbs Down? Sentiment Orientation Applied to Unsupervised Classification of Reviews, Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL); p. 417-424. https://doi.org/10.3115/1073083.1073153.

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  • A Comprehensive Classification of Sentiment Reviews of Twitter Data in the Domain of Climatology using Machine Learning Techniques

Abstract Views: 356  |  PDF Views: 5

Authors

Apala Chatterjee
Department of Library and Information Science, Jadavpur University, Kolkata − 700032, West Bengal, India
Shampa Mahato
Department of Library and Information Science, Jadavpur University, Kolkata − 700032, West Bengal, India
Sunil Kumar Chatterjee
Department of Library and Information Science, Jadavpur University, Kolkata − 700032, West Bengal, India

Abstract


Purpose: This study aims at classification of sentiment reviews of Twitter data in the domain of climatology using machine learning techniques. It focuses on the text classification in order to determine the people’s intension about the climatic issues i.e., climate change, climate variability, environmental aspects etc. This paper portrays a comparison of results obtained by applying different classification algorithms like Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbour (KNN), Decision Tree Classifier, Neural Network classifier etc. These algorithms are used to classify a sentimental review and people’s emotions associated with climate. Design/Methodology/Approach: Total 2265 climate reviews data have been taken from Twitter’s developers’ account. After that, we pre-processed the total dataset by removing various symbols, HTTP tags, punctuation, etc. The pre-processed text were analysed and represented through Topic modelling, Multi Dimensional Scaling (MDS) and also Visualization of Heatmap. Next, bag of words are evaluated through various algorithms such as Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbour (KNN), Decision Tree Classifier and Neural Network classifier. After applying above mentioned classifier, datasets are tested and scores are noted. For the experiment, 70 % of total reviews (i.e.1586) are used for model training and 30% of total reviews (i.e. 680) are used for testing the models. Findings: By performing different algorithms, it shows that Random Forest classifier algorithm works well than other mentioned classifiers and most of the people have positive sentiment towards climate according to Valence Aware Dictionary for Sentiment Reasoning (VADER).

Keywords


Algorithms Classifier Techniques, Machine Learning Techniques, Polarity, Opinion Mining, Reviews, Sentiment Analysis.

References