A Comprehensive Classification of Sentiment Reviews of Twitter Data in the Domain of Climatology using Machine Learning Techniques
Authors
-
Department of Library and Information Science, Jadavpur University, Kolkata − 700032, West Bengal
Apala Chatterjee -
Department of Library and Information Science, Jadavpur University, Kolkata − 700032, West BengalShampa Mahato
-
Department of Library and Information Science, Jadavpur University, Kolkata − 700032, West BengalSunil Kumar Chatterjee
DOI:
https://doi.org/10.17821/srels/2022/v59i3/168281Keywords:
Algorithms Classifier Techniques, Machine Learning Techniques, Polarity, Opinion Mining, Reviews, Sentiment AnalysisAbstract
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).
Downloads
Metrics
References
Dave, K., Lawrence, S. and Pennock, D. M. (2003, May). Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews. In: Proceedings of the 12th International Conference on World Wide Web; p. 519-528. https://doi.org/10.1145/775152.775226. DOI: https://doi.org/10.1145/775152.775226
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. DOI: https://doi.org/10.1037/0022-3514.52.6.1061
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. DOI: 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. DOI: 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. DOI: 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. DOI: 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. DOI: 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. DOI: https://doi.org/10.3115/1220575.1220619
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. DOI: https://doi.org/10.3115/1073083.1073153
Hatzivassiloglou, V. (2000). Effects of Adjective Orientation and Grad Ability on Sentence Subjectivity. COLING ‘OO: Proceedings of the 18th Conference on Computational Linguistics, 1: 299-305. https://doi.org/10.3115/990820.990864. DOI: https://doi.org/10.3115/990820.990864
Jabreel, M. and Ribas, A. M. (2017, August). SiTAKA at SemEval-2017 Task 4: Sentiment Analysis in Twitter Based on a Rich Set of Features. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017); p. 694-699. https://doi.org/10.18653/v1/S17-2115. DOI: https://doi.org/10.18653/v1/S17-2115
Jianqiang, Z., Xiaolin, G. and Xuejun, Z. (2018). Deep convolution neural networks for twitter sentiment analysis. IEEE Access, 6, 23253-23260. https://doi.org/10.1109/ACCESS.2017.2776930. DOI: https://doi.org/10.1109/ACCESS.2017.2776930
Downloads
Published
How to Cite
Issue
Section
License
All the articles published in Journal of Information and Knowledge are held by the Publisher. Sarada Ranganathan Endowment for Library Science (SRELS), as a publisher requires its authors to transfer the copyright prior to publication. This will permit SRELS to reproduce, publish, distribute and archive the article in print and electronic form and also to defend against any improper use of the article.
