Abstract
Sentiment classification is a technique to understand the feeling/attitude/sentiment toward a written piece of text by analyzing and then classifying the text as positive, negative, or neutral. One of the important aspects of classification is data that should be handled and represented carefully in the classification process, which affects the performance of the classifier. In the sentiment classification process, feature vector is used as the representation of data to work on. In the paper, we have experimented with the combination of Word2vec and Bag-of-Centroids’ feature vector in the sentiment classification process of online consumer reviews about different mobile brands. The feature vector is tested on different well-known machine learning classifiers used for sentiment analysis and compared with Word2vec feature vector. We also investigated the performance of a feature vector as the size of the dataset is increased. We found that the proposed feature vector performed well in comparison with Word2vec feature vector.
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Choudhari, P., Veenadhari, S. (2020). Sentiment Classification of Online Mobile Reviews Using Combination of Word2vec and Bag-of-Centroids. In: Swain, D., Pattnaik, P., Gupta, P. (eds) Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1101. Springer, Singapore. https://doi.org/10.1007/978-981-15-1884-3_7
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DOI: https://doi.org/10.1007/978-981-15-1884-3_7
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