Abstract
Sentiment analysis is used to derive the emotion/opinion that is being conveyed in a text. This helps in determining whether the author’s intent is positive or negative. Its applications are vast and help in analyzing product reviews, popularity of a brand, and in determining people’s opinions on any subject. Due to the complexities involved in the human language such as subjectivity, metaphors, sarcasm, and multiple sentiments, it becomes difficult to categorize our opinions, computationally. The goal of this project is to conduct sentiment analysis on Amazon product reviews using various natural language processing (NLP) techniques and classification algorithms. The dataset consists of 400,000 reviews of unlocked mobile phones sold on Amazon.com. We will achieve the result by preprocessing the reviews and converting them to clean reviews, after which using word embedding, the word reviews were converted into numerical representations. Then, we finally fit the numerical representations of reviews to the Naïve Bayes, logistic regression, and random forest algorithm. The results and accuracy of all these classifiers are compared in this paper. This will be helpful for a brand/company to understand the general opinion toward their product which in turn will help them in evaluating the improvements required.
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Meenakshi, Banerjee, A., Intwala, N., Sawant, V. (2020). Sentiment Analysis of Amazon Mobile Reviews. In: Tuba, M., Akashe, S., Joshi, A. (eds) ICT Systems and Sustainability. Advances in Intelligent Systems and Computing, vol 1077. Springer, Singapore. https://doi.org/10.1007/978-981-15-0936-0_4
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DOI: https://doi.org/10.1007/978-981-15-0936-0_4
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