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                <text>Conference Papers</text>
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              <text>Advanced Sentiment Analysis: From Lexicon-Enhanced BERT to Dimensionality Reduction Using NLP</text>
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              <text>BERT Embeddings; Lexicon-Based Sentiment Analysis; Natural Language Processing (NLP); Sentiment Analysis in Many Different Languages</text>
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              <text>Social media platforms serve as vital connections for communication, generating massive quantities of data that represent an array of perspectives. Efficient sentiment analysis is necessary for understanding public opinion, particularly in domains such as product reviews and socio-political discussion. This paper develops a novel sentiment analysis model that is customized for social media data by integrating machine learning algorithms, language processing techniques with part-of-speech tagging, and dimensionality reduction methods. The model will improve sentiment analysis performance by tackling challenges like noise and data domain variations. To further improve sentiment representation, it includes convolutional neural networks (CNNs), BERT embeddings, N-grams, and sentiment lexicons. The model's effectiveness is determined on a variety of datasets, which enhances sentiment analysis in social media discussion. This paper goes beyond sentiment analysis in code-mixed, multilingual text and highlights the importance of careful data before treatment and an extensive variety of ML algorithms. This study attempts to explain the nuances of sentiment analysis and its use in social media discussions through methodical research.   2024 IEEE.</text>
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              <text>Kumar V.; Chauhan P.S.; Lella J.A.; Momidi M.; Gupta P.; Upreti K.</text>
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              <text>2024 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024 - Proceedings, pp. 441-446.</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>&lt;a href="https://doi.org/10.1109/ETNCC63262.2024.10767483" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ETNCC63262.2024.10767483&lt;/a&gt;
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              <text>ISBN: 979-835035326-6</text>
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              <text>Kumar V., SEAS, GWU, Washington, DC, United States; Chauhan P.S., Global Payments, Atlanta, United States; Lella J.A., Electrical &amp;amp; Comm. Engg., National Institute of Technology, Calicut, India; Momidi M., Dell Technologies, Bangalore, India; Gupta P., Computer Science Engineering, ADGIPS, Delhi, India; Upreti K., Christ University, Dept. of Computer Science, Delhi NCR Campus, India</text>
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