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              <text>Utilizing Machine Learning for Advanced Natural Language Processing and Sentiment Analysis in Social Media Platforms</text>
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              <text>Machine learning (ML); Natural language processing; Random forest (RF); Sentiment analysis; Support vector machine (SVM)</text>
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              <text>Social media is increasingly regarded as one of the most abundant online resources for information gathering and knowledge exchange. Among the most widely used social media sites is Twitter available today. When attempting to comprehend the information in any unknown word-based data (such as social media), natural language processing (NLP) techniques are crucial since they help remove noise from data, identify stem words, etc. It also helps with comprehension of the sentiment or semantic contents. Using social media, we apply machine learning techniques (clustering and classification) to determine the viewpoint's polarity in the information. Several classifiers and clusters, including SVM, RF, Naive Byes, and KNN, are used to detect content on social media. Sentiment analysis is the process of automatically classifying user-generated content as neutral, negative, or positive. It is possible to utilize the text, sentence, feature, or aspect as criteria to group feelings into distinct categories. This study demonstrates the application of machine learning techniques to the analysis of emotions expressed on the Twitter network.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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              <text>Manoharan G.; Jyothi B.S.; Bhat R.; Acharjee P.B.; Sharma A.; Rawal P.</text>
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              <text>Smart Innovation, Systems and Technologies, Vol-390, pp. 111-125.</text>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>2024-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-97-2716-2_12" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-97-2716-2_12&lt;/a&gt;
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              <text>Restricted Access</text>
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              <text>ISSN: 21903018; ISBN: 978-981972715-5</text>
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              <text>Online</text>
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              <text>Manoharan G., School of Business, SR University, Telangana, Warangal, India; Jyothi B.S., CSE (DS), Rajeev Gandhi Memorial College of Engineering and Technology, Nandyala, India; Bhat R., Amity School of Communication, Amity University Kolkata, Kolkata, India; Acharjee P.B., Computer Science, CHRIST University Pune Lavasa Campus, Lavasa, India; Sharma A., Jagran Lakecity Business School, Faculty of Management and Commerce, Jagran Lakecity University, Madhya Pradesh, Bhopal, India; Rawal P., Faculty of Management and Commerce, Jagran Lakecity University, Madhya Pradesh, Bhopal, India</text>
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