Sentiment and Emotion Classification of Epidemic Related Bilingual data from Social Media

• URL: http://arxiv.org/abs/2105.01468v1
• Date: Tue, 4 May 2021 12:51:18 GMT
• Title: Sentiment and Emotion Classification of Epidemic Related Bilingual data from Social Media
• Authors: Muhammad Zain Ali, Kashif Javed, Ehsan ul Haq, Anoshka Tariq
• Abstract summary: The study exploits the bilingual (Urdu and English) data from Twitter and NEWS websites related to the dengue epidemic in Pakistan. The proposed study exploits the bilingual (Urdu and English) data from Twitter and NEWS websites related to the dengue epidemic in Pakistan.
• Score: 1.7109522466982476
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, sentiment analysis and emotion classification are two of the most abundantly used techniques in the field of Natural Language Processing (NLP). Although sentiment analysis and emotion classification are used commonly in applications such as analyzing customer reviews, the popularity of candidates contesting in elections, and comments about various sporting events; however, in this study, we have examined their application for epidemic outbreak detection. Early outbreak detection is the key to deal with epidemics effectively, however, the traditional ways of outbreak detection are time-consuming which inhibits prompt response from the respective departments. Social media platforms such as Twitter, Facebook, Instagram, etc. allow the users to express their thoughts related to different aspects of life, and therefore, serve as a substantial source of information in such situations. The proposed study exploits the bilingual (Urdu and English) data from Twitter and NEWS websites related to the dengue epidemic in Pakistan, and sentiment analysis and emotion classification are performed to acquire deep insights from the data set for gaining a fair idea related to an epidemic outbreak. Machine learning and deep learning algorithms have been used to train and implement the models for the execution of both tasks. The comparative performance of each model has been evaluated using accuracy, precision, recall, and f1-measure.

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