Abstract
Sentiment analysis determines the sentiment or opinion of a given text. A sentiment analysis model can classify whether a given text data is positive or negative by extracting meaning from the natural language. The growth of social media such as Twitter, forum discussions and reviews, contributed to the huge data repository in digital form. Analyzing these huge data manually is very time consuming and challenging. Thus, applying machine learning techniques can automatically classify the sentiment effectively. This research compares the performance of five popular machine learning techniques for sentiment analysis namely, Support Vector Machine (SVM), Logistic Regression, Naïve Bayes, Random Forest and K-Nearest Neighbor using a publicly available dataset from kaggle.com. Their classification performances are compared based on accuracy and training time where fine tuning of some of the hyperparameters are per-formed to improve the accuracy. Experimental analysis indicates that SVM with linear kernel function produces the highest accuracy but a slower training time. On the other hand, Naïve Bayes requires the shortest training time but with a slightly lower accuracy compared to SVM.
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Ahmad Hapez, M.H.I., Adam, N.L., Ibrahim, Z. (2021). Performance Analysis of Machine Learning Techniques for Sentiment Analysis. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2021. Lecture Notes in Computer Science(), vol 13051. Springer, Cham. https://doi.org/10.1007/978-3-030-90235-3_18
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