Sentiment Analysis Using Machine Learning Approach

Andreea-Maria Copaceanu ()
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Andreea-Maria Copaceanu: The Bucharest University of Economic Studies

Ovidius University Annals, Economic Sciences Series, 2021, vol. XXI, issue 1, 261-270

Abstract: Customers feedback is a valuable asset for businesses, that can be used in order to improve their performance. One of the fastest spreading areas today in computer science - Sentiment Analysis, helps to extract precious information from textual data, in order to identify the feeling of a statement. This research aims to build a classifier to predict customers’ satisfaction, based on Amazon reviews dataset, for different brands of mobile phones. The paper proposes a comparison between four text classification algorithms - Naïve Bayes, Support Vector Machine, Decision Tree and Random Forest, using different feature extraction techniques, such as Bag of words and TF-IDF. In addition, the models are evaluated using accuracy, precision, recall and F-score metrics. Our experiments revealed that Support Vector Machine achieves the best results and is very suitable for classification of the sentiment on product reviews.

Keywords: Sentiment analysis; customer reviews; machine learning; text classification (search for similar items in EconPapers)
JEL-codes: A12 L21 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:ovi:oviste:v:xxi:y:2021:i:1:p:261-270

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