Abstract
If consumers want something today, the manufacturer cannot let them wait until tomorrow. All businesses need to stay updated on opinion fluctuations over existing products or services so that they can improve upon and can stay ahead in this tough competition. This field of market research is called sentiment analysis and requires structuring of unstructured online text data on a vast scale. Various techniques have already been put forward for this. In this paper, we will study the performance of broadly four such techniques—K nearest neighbours, logistic regression, support vector machine and Naive Bayes, when they are applied on a large dataset of 8500 movie reviews. The comparison and conclusion will be drawn out on the basis of accuracy, precision score, training and testing time.
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Joshi, S., Dubey, R., Tiwari, A., Jindal, P. (2021). Sentiment Analysis Algorithms: Classifiers and Their Comparison. In: Singh, B., Coello Coello, C.A., Jindal, P., Verma, P. (eds) Intelligent Computing and Communication Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-1295-4_21
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DOI: https://doi.org/10.1007/978-981-16-1295-4_21
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