Sentiment analysis of weather news in British online newspapers
Keywords:
text, emotion, natural language, negative/positive sentiment, polarity, sentiment analysis, subjectivity, weather news.
Abstract
The advancement of modern technologies has influenced the way news is presented and consumed, particularly online. Weather is an important topic for the public as it relates to the human experience and addresses current societal issues. In this paper, we introduce a systematic approach to conduct sentiment analysis of weather news stories, to specify the emotional tone and examine the role of subjectivity in online news reporting. This research falls within the scope of a lexicon-based (unsupervised) approach to sentiment analysis, which involves finding the sentiment polarity of words. The analysis is predominantly based on sentence-level sentiment analysis. Two popular online web services, MonkeyLearn and SentiStrength, were applied to automatically detect human emotions. We compared the efficiency of each tool and found that MonkeyLearn provided better final results in comparison to SentiStrength, which tended to misclassify negative sentiments into neutral ones. The final results of frequency calculation showed the dominance of weather news stories with negative sentiment polarity over positive and neutral ones, with neutral sentiments being in the minority. Based on the empirical findings, we observed an objectivity-to-subjectivity shift in online news reporting.
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References
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Al-Omari, H., Abdullah, M., & Bassam, N. (2019). EmoDet at SemEval-2019 Task 3: Emotion Detection in Text using Deep Learning. International Workshop on Semantic Evaluation.
Amin, S., Uddin, M.I., Al-Baity, H.H., Zeb, M.A., & Khan, M.A. (2021). Machine Learning Approach for COVID-19 Detection on Twitter. Comput. Mater. Contin., 68, 2231–2247.
Beigi, O.M., & Moattar, M.H. (2020). Automatic construction of domain-specific sentiment lexicon for unsupervised domain adaptation and sentiment classification. Knowl. Based Syst., 213, 106423.
Bekhta, I., & Hrytsiv, N. (2021). Computational Linguistics Tools in Mapping Emotional Dislocation of Translated Fiction. International Conference on Computational Linguistics and Intelligent Systems.
Bhatia, T. K., Chauhan, K., & Suden, R. (2022). “A Novel Technique to Detect the Fake News by Using the Machine Learning Approaches.” 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 1-6
Blake J. S. (2019). News in a Digital Age: Comparing the Presentation of News Information over Time and Across Media Platforms. Rand Corporation.
Boesman, J., & Costera-Meijer, I.C. (2018). “Don’t read me the news, tell me the story”: How news makers and storytellers negotiate journalism’s boundaries when preparing and presenting news stories. pp. 13‒32.
Bondarchuk, N., & Bekhta, I. (2021). Quantitative Characteristics of Lexical-Semantic Groups Representing Weather in Weather News Stories (Based on British Online Press). International Conference on Computational Linguistics and Intelligent Systems
Bonta, V., Kumaresh, N., & Janardhan, N. (2019). A Comprehensive Study on Lexicon Based Approaches for Sentiment Analysis. Asian Journal of Computer Science and Technology, 8, pp. 1–6.
Chaturvedi, I., Cambria, E., Welsch, R.E., & Herrera, F. (2018). Distinguishing between facts and opinions for sentiment analysis: Survey and challenges. Inf. Fusion, 44, 65-77. https://doi.org/10.1016/j.inffus.2017.12.006.
Dagar, V., Verma, A., & Govardhan, K. (2021). Sentiment analysis and sarcasm detection (using emoticons). In: Applications of artificial intelligence for smart technology. IGI Global, pp 164–176
D’Aniello, G., Gaeta, M., & La Rocca, I. (2022). KnowMIS-ABSA: An overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis. Artif. Intell. Rev. 1–32.
Dharmale, G., Karjagi, S., Kotalwar, H., & Khobragade, S. A. (2023) Detailed Survey on Sentimental Analysis on Social Media. In: Gunjan, V.K., Zurada, J.M. (eds) Proceedings of 3rd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. Lecture Notes in Networks and Systems, vol 540. Springer, Singapore. https://doi.org/10.1007/978-981-19-6088-8_22
Garay, J., Yap, R., & Sabellano, M. J. (2019). An analysis on the insights of the anti-vaccine movement from social media posts using k-means clustering algorithm and VADER sentiment analyser IOP Conference Series: Materials Science and Engineering, 482(1), 012043
Gupta, S., & Urvashi, S. A. (2023). A Vocabulary-Based Framework for Sentiment Analysis. In: Shukla, A., Murthy, B.K., Hasteer, N., Van Belle, JP. (eds) Computational Intelligence. Lecture Notes in Electrical Engineering, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-19-7346-8_43
Höller, M. (2021). The human component in social media and fake news: the performance of UK opinion leaders on Twitter during the Brexit campaign. European Journal of English Studies, 25, 80 - 95.
Hota, H.S., Sharma, D.K., & Verma, N. (2021). Lexicon-based sentiment analysis using Twitter data. Data Science for COVID-19, 275-295. doi: 10.1016/B978-0-12-824536-1.00015-0
Jurafsky, D., & H Martin, J. (2021). Naive Bayes and sentiment classification. In: Speech and language processing. Stanford University.
Mahmood, A., Kamaruddin, S., Naser, R., & Nadzir, M. (2020). A combination of lexicon and machine learning approaches for sentiment analysis on Facebook. J. Syst. Manag. Sci., 10, 140–150.
Mohamad Sham, N., & Mohamed, A.H. (2022). Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches. Sustainability. https://doi.org/10.3390/su14084723
Mutinda, J., Mwangi, W., & Okeyo, G.O. (2020). Lexicon‐pointed hybrid N‐gram Features Extraction Model (LeNFEM) for sentence level sentiment analysis. Engineering Reports, 3.
Wiebe, J., & Riloff, E. (2005). Creating Subjective and Objective Sentence Classifiers from Unannotated Texts. Conference on Intelligent Text Processing and Computational Linguistics.
Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., Cardie, C., Riloff, E., & Patwardhan, S. (2019). "Opinion Finder: A system for subjectivity analysis". In Proceedings of hlt/emnlp on interactive demonstrations, pp. 34-35.
Yates, J., & Orlikowski, W. J. (1992). Genres of organizational communication: A structurational approach. Academy of Management Review, 17, pp. 299–326.
Zhou, Z. (2021). Emotional thinking as the foundation of consciousness in artificial intelligence. Cultures of Science, 4, 112-123. https://doi.org/10.1177/20966083211052651
Published
2023-04-30
How to Cite
Bondarchuk, N., Hrytsiv, N., Bekhta, I., & Melnychuk, O. (2023). Sentiment analysis of weather news in British online newspapers. Amazonia Investiga, 12(63), 99-108. https://doi.org/10.34069/AI/2023.63.03.9
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