This paper explores the application of Natural Language Processing (NLP) techniques for sentiment analysis in the context of social media data. The rapid growth of social media platforms has led to an exponential increase in the volume of textual data, necessitating the development of effective methods to analyze and interpret this information. Sentiment analysis, a subfield of NLP, aims to determine the sentiment or opinion expressed in a piece of text, which is invaluable for understanding public opinion, tracking trends, and making data-driven decisions. This study reviews various NLP methods employed for sentiment analysis, including text preprocessing, feature extraction, and classification algorithms. The impact of these techniques on the accuracy of sentiment analysis is discussed, highlighting the challenges and opportunities in this area. Additionally, the paper examines the application of sentiment analysis in real-world scenarios such as brand monitoring, political analysis, and customer service. Finally, the limitations and future directions of NLP for sentiment analysis in social media are identified, emphasizing the need for improved algorithms, better preprocessing techniques, and a deeper understanding of the nuances of human language.
Anderson, E. Natural Language Processing for Sentiment Analysis in Social Media. Information Sciences and Technological Innovations, 2023, 5, 37. https://doi.org/10.69610/j.isti.20230212
AMA Style
Anderson E. Natural Language Processing for Sentiment Analysis in Social Media. Information Sciences and Technological Innovations; 2023, 5(1):37. https://doi.org/10.69610/j.isti.20230212
Chicago/Turabian Style
Anderson, Emily 2023. "Natural Language Processing for Sentiment Analysis in Social Media" Information Sciences and Technological Innovations 5, no.1:37. https://doi.org/10.69610/j.isti.20230212
APA style
Anderson, E. (2023). Natural Language Processing for Sentiment Analysis in Social Media. Information Sciences and Technological Innovations, 5(1), 37. https://doi.org/10.69610/j.isti.20230212
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