This paper explores the application of Natural Language Processing (NLP) techniques in sentiment analysis of social media data. With the exponential growth of social media platforms, vast amounts of textual data are being generated daily. Analyzing the sentiment behind this data can provide valuable insights for businesses, policymakers, and researchers. The study delves into the challenges faced in sentiment analysis, including the complexity of language, subjectivity, and contextual nuances. It presents a comprehensive review of various NLP methods, such as machine learning, deep learning, and ensemble techniques, employed for sentiment analysis. The paper further discusses the importance of preprocessing steps like text cleaning, tokenization, and feature extraction. Additionally, it evaluates the performance of different models on social media datasets and highlights the limitations and future directions for research in this field. By providing a thorough understanding of NLP-based sentiment analysis, this paper aims to contribute to the development of more accurate and efficient models for extracting sentiments from social media data.
Brown, S. Natural Language Processing for Sentiment Analysis in Social Media Data. Transactions on Applied Soft Computing, 2023, 5, 39. https://doi.org/10.69610/j.tasc.20230516
AMA Style
Brown S. Natural Language Processing for Sentiment Analysis in Social Media Data. Transactions on Applied Soft Computing; 2023, 5(1):39. https://doi.org/10.69610/j.tasc.20230516
Chicago/Turabian Style
Brown, Sarah 2023. "Natural Language Processing for Sentiment Analysis in Social Media Data" Transactions on Applied Soft Computing 5, no.1:39. https://doi.org/10.69610/j.tasc.20230516
APA style
Brown, S. (2023). Natural Language Processing for Sentiment Analysis in Social Media Data. Transactions on Applied Soft Computing, 5(1), 39. https://doi.org/10.69610/j.tasc.20230516
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