Authors: Anurag Jadhav , Vaishnavi Bolli , Sanket Kewale, Atharva Kharat
DOI Link: https://doi.org/10.22214/ijraset.2023.56738
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This study investigates multilingual sentiment analysis within tweets on ChatGPT, an AI conversational model, employing Support Vector Machines (SVM) and BERT, an advanced language model. It aims to detect and classify emotions, including emoji identification, embedded within diverse messages across multiple languages on Twitter. By leveraging SVM\'s text classification and BERT\'s contextual understanding in various languages, the research delves into preprocessing techniques and feature engineering for sentiment analysis, encompassing multilingual and emoji detection. Furthermore, it explores the fusion of traditional SVM methods with BERT\'s state-of-the-art model for multilingual sentiment analysis, emphasizing emotion and emoji detection in AI-generated content on multilingual social media platforms like Twitter. This research yields insights into the successful detection of multilingual sentiment nuances and emotions, including emoji identification. It offers implications for advancing multilingual sentiment analysis in natural language processing across diverse linguistic contexts.
I. INTRODUCTION
In this research, we aim to analyze the sentiments expressed in tweets related to OpenAI's ChatGPT utilizing text processing methodologies and machine learning algorithms. Twitter, being a platform that offers real-time and concise messages, provides a substantial dataset for this examination. Our study will detail the methodology, encompassing data collection, processing techniques, analytical methods, and the resultant findings from this sentiment analysis. Conventional sentiment analysis often falls short in comprehending intricate emotions and contextual nuances. To address these limitations, our approach focuses on three fundamental aspects: identifying emotions, scrutinizing specific contextual elements, and accommodating multilingual capabilities. By amalgamating these features, our advanced sentiment analysis system strives to offer a more nuanced, precise, and language-independent comprehension of emotions embedded within textual content. This approach aims to overcome the constraints observed in traditional sentiment analysis techniques.
II. LITERATURE REVIEW
III. METHODOLOGY
In conclusion, the SVM and BERT models used in the Twitter sentiment analysis of ChatGPT tweets have yielded important insights about the tone of emotion in the content that has been created. The SVM model performed admirably in capturing the general sentiment patterns because of its capacity to categorize tweets according to a set of attributes. Conversely, the BERT model demonstrated a sophisticated understanding of the feelings expressed in the tweets by utilizing its contextual knowledge of language. The power of fusing cutting-edge deep learning techniques like BERT with conventional machine learning methods like SVM was made evident by the incorporation of these models into the sentiment analysis pipeline. With this hybrid technique, sentiment analysis across a wide range of lively Twitter conversations may be done in-depth.
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Copyright © 2023 Anurag Jadhav , Vaishnavi Bolli , Sanket Kewale, Atharva Kharat . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET56738
Publish Date : 2023-11-17
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here