AI Sentiments Analysis for Social Media

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Varun Pratap; Shiv Sharan Dixit; Arpit Negi; Himani Aggarwal; Shubham Kumar; Anupam Sharma

AI Sentiments Analysis for Social Media

Authors: Varun Pratap, Shiv Sharan Dixit, Arpit Negi, Himani Aggarwal, Shubham Kumar, Anupam Sharma

Unique Paper ID: 169419
Volume: 11
Issue: 6
PageNo: 1249-1255

Keywords: AI sentiment analysis social media machine learning natural language processing deep learning BERT GPT public opinion real-time analysis.

Abstract:
Social media platforms have become a significant source of public opinion, providing vast amounts of unstructured textual data that reflects people's thoughts, attitudes, and emotions. Sentiment analysis, the process of identifying and categorizing sentiments expressed in this data, has evolved with the rise of artificial intelligence (AI). This paper explores AI-powered sentiment analysis, highlighting the role of machine learning and natural language processing (NLP) in enhancing the accuracy and efficiency of analyzing social media data. The study reviews traditional sentiment analysis methods and contrasts them with modern AI approaches such as deep learning models and transformers like BERT and GPT. It also discusses challenges in AI-driven sentiment analysis, such as sarcasm detection, multilingual text processing, and contextual understanding, while outlining key applications across industries, including marketing, politics, finance, and public health. The research concludes by examining the future potential of AI technologies in refining sentiment analysis for more accurate real-time insights. The research emphasizes the potential of AI to further enhance the capability of sentiment analysis, making it a valuable tool for interpreting social media data in real time and on a large scale.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 6
PageNo: 1249-1255

AI Sentiments Analysis for Social Media

Available:https://ijirt.org/article?manuscript=169419

07 Nov, 2024

AI Sentiments Analysis for Social Media

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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