International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Anatomy of Sentiment Analysis of Tweets Using Machine Learning Approach

  • Misbah Iram (University Institute of Information Technology, PMAS – University of Arid Agriculture) ;
  • Saif Ur Rehman (University Institute of Information Technology, PMAS – University of Arid Agriculture) ;
  • Shafaq Shahid (University Institute of Information Technology, PMAS – University of Arid Agriculture) ;
  • Sayeda Ambreen Mehmood (University Institute of Information Technology, PMAS – University of Arid Agriculture)
  • Received : 2023.10.05
  • Published : 2023.10.30
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Abstract

Sentiment analysis using social network platforms such as Twitter has achieved tremendous results. Twitter is an online social networking site that contains a rich 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 is important to engage the targeted population. In this work, numerous methods that perform a classification of tweet sentiment in Twitter is discussed. There has been a lot of work in the field of sentiment analysis of Twitter data. This study provides a comprehensive analysis of the most standard and widely applicable techniques for opinion mining that are based on machine learning and lexicon-based along with their metrics. The proposed work is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous, and polarized positive, negative or neutral. In order to validate the performance of the proposed framework, an extensive series of experiments has been performed on the real world twitter dataset that alter to show the effectiveness of the proposed framework. This research effort also highlighted the recent challenges in the field of sentiment analysis along with the future scope of the proposed work.

Keywords

Acknowledgement

The authors would like to express their cordial thanks to Dr. Mitsuo Ohta for his valuable advice.

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