Abstract
Text classification is a very important problem in artificial intelligence domain and covers a wide portion in natural language processing, which can be called as sentiment analysis. Sentiment analysis is basically extracting the tone or emotion of the writer, by understanding the text sequence. This way of approach is to understand the sentiment of a text considering as a boon in the customer management system and can easily be applied to the social media sites, such as twitter or e-commerce websites, like amazon to get the customer review and analyze. Sentiment analysis can be binary or multiclass, here in our approach, we will consider both of them, by doing a comparative study between long short-term memory (LSTM), random forest, support vector machine(SVM), and XGBoost, to check if they can be as good as LSTM in any case. Also, as we discover the data distribution problem in our datasets, so we will be applying oversampling to make the distribution in a stabilized form.
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Chatterjee, D.P., Mukhopadhyay, S., Goswami, S., Panigrahi, P.K. (2021). Efficacy of Oversampling Over Machine Learning Algorithms in Case of Sentiment Analysis. In: Sharma, N., Chakrabarti, A., Balas, V.E., Martinovic, J. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1175. Springer, Singapore. https://doi.org/10.1007/978-981-15-5619-7_17
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DOI: https://doi.org/10.1007/978-981-15-5619-7_17
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