Abstract
The social media platforms enable their users to provide feedback and voice complaints about the services and goods they have used. Sentiment analysis is a powerful tool that can help the software industry and company to better evaluate user needs and cater the software in a way to maximize the sales potential. One of the studying areas in natural language process (NLP) is sentiment analysis which is concerned for identifying the opinion or mood within a text. For extracting the information from the social big data, an automatic procedure is essential for decision-makers and marketers. For satisfying this requirement, an automatic sentiment analysis saleability prediction for information extraction using SentiStrength algorithm is presented in this paper. From consumers, data is collected through feedback forms on software product. Presented algorithm validity is proven through comparing the contrast rule-based sentiment analysis (CRbSA), general word counting, and extraction algorithms well-known sentiment information. Accuracy and processing time are two parameters used to analyze the performance of SentiStrength algorithm, and these values are 81.5% as accuracy and 15 ms as processing time. In a marketing system, this algorithm is employed for extracting the satisfaction of customers in particular, it serves as a warning mechanism for unfavorable remarks.
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Gouthami, S., Hegde, N.P. (2023). Automatic Sentiment Analysis Scalability Prediction for Information Extraction Using SentiStrength Algorithm. In: Reddy, A.B., Nagini, S., Balas, V.E., Raju, K.S. (eds) Proceedings of Third International Conference on Advances in Computer Engineering and Communication Systems. Lecture Notes in Networks and Systems, vol 612. Springer, Singapore. https://doi.org/10.1007/978-981-19-9228-5_3
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DOI: https://doi.org/10.1007/978-981-19-9228-5_3
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