Anatomy of Sentiment Analysis of Tweets Using Machine Learning Approach
| Autor: | Misbah Iram, Saif Ur Rehman, Shafaq Shahid, Sayeda Ambreen Mehmood |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Proceedings of the Pakistan Academy of Sciences: A. Physical and Computational Sciences. 59:61-73 |
| ISSN: | 2518-4253 2518-4245 |
| DOI: | 10.53560/ppasa(59-2)771 |
| Popis: | Sentiment Analysis (SA) is an efficient way of determining people’s opinions from a piece of text. SA using different social media sites such as Twitter has achieved tremendous results. Twitter is an online social media platform that contains a massive amount of data. The platform is known as an information channel corresponding to different sites and categories. Tweets are most often publicly accessible with very few limitations and security options available. Twitter also has powerful tools to enhance the utility of Twitter and a powerful search system to make publicly accessible the recently posted tweets by keyword. As popular social media, Twitter has the potential for interconnectivity of information, reviews, updates, and all of which are important to engage the targeted population. In this work, numerous methods that perform a classification of tweet sentiment on Twitter have been discussed. There has been an extensive research studies in the field of SA of Twitter data. This study provides a comprehensive analysis of the most standard and widely applicable opinion mining techniques based on machine learning and lexicon-based along with their metrics. The proposed work is helpful in informaiton analysis in the tweets where opinions are found heterogeneous, unstructured, polarised negative, positive, or neutral. In order to validate the supremacy of the suggested approach, we have executed a series of experiments on the real-world Twitter dataset that alters to show the effectiveness of the proposed framework. This research effort also highlighted the recent challenges in the SA field and the proposed work’s future scope. |
| Databáze: | OpenAIRE |
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