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Mahato, Shampa
- Deriving Pertinent Knowledge through Sentiment Analysis and Linking with Relevant Documents
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PDF Views:5
Authors
Affiliations
1 Department of Library and Information Science, Jadavpur University, Kolkata, West Bengal, IN
1 Department of Library and Information Science, Jadavpur University, Kolkata, West Bengal, IN
Source
Journal of Information and Knowledge (Formerly SRELS Journal of Information Management), Vol 58, No 5 (2021), Pagination: 319-331Abstract
Purpose: This study aims to explore pertinent knowledge through the Sentiment Analysis technique and to link with relevant, pin-pointed documents. Design/Methodology/Approach: While information is essential ‘information overload’ is a big problem when we search for specific information. To get rid of psychological stress, mistakes in decision making or disregarding of relevant information, a methodology has been developed which may be suitable for researchers to extract pertinent knowledge from huge amount of research publications in a particular domain (‘climatology’ has been chosen for demonstration) within the shortest possible time. The study presents, how exactly relevant information can be retrieved there through sentiment analysis and through which a preliminary knowledge base can be gained. For this, ‘R’ software has been used to do the desired manipulation on the collected data. The steps involve pre-processing of introductory text, tokenization, polarity detection and analysis of text through sentiment analysis. Findings: It has been found that knowledge derived through sentiment analysis and abstract of the linked documents fairly match with each other, which validates the relevance and importance of the linked documents. Again, the impact factor of the prestigious journal having global coverage, where most of the linked documents were published also shows the importance of the linked documents/papers.Keywords
Information Extraction, Information Overload, Pertinent Knowledge, Polarity Dataset, Sentiment Analysis, Subjective Analysis.
References
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- 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.
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- 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.
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- Medhat, W. (2014). Sentiment analysis algorithms and applications: A survey. A in Shams Engineering Journal, 5(4): 1093-1113. https://doi.org/10.1016/j.asej.2014.04.011.
- 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.
- Iqbal, F. (2019). A hybrid framework for sentiment analysis using genetic algorithm based feature reduction. IEEE Access, 99.
- 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 :228 |
PDF Views:5
Authors
Affiliations
1 Department of Library and Information Science, Jadavpur University, Kolkata − 700032, West Bengal, IN
1 Department of Library and Information Science, Jadavpur University, Kolkata − 700032, West Bengal, IN
Source
Journal of Information and Knowledge (Formerly SRELS Journal of Information Management), Vol 59, No 3 (2022), Pagination: 141-151Abstract
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
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- 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.
- 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.
- 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.
- 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.