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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Waykole, R.N., Thakare, A.D.: A Review of feature extraction methods for text classification. Int. J. Adv. Eng. Res. Dev. 5(04) e-ISSN (O): 2348–4470, p-ISSN (P): 2348-6406 (2018)
Poecze, F., Ebsterb, C., Strauss, C.: Social media metrics and sentiment analysis to evaluate the effectiveness of social media posts. In: The 9th International Conference on Ambient Systems, Networks, and Technologies, ANT 2018, Procedia Computer Science, vol. 130, pp. 660–666 (2018)
Ankit, Saleena, N.: An ensemble classification system for twitter sentiment analysis. In: International Conference on Computational Intelligence and Data Science, ICCIDS 2018, Procedia Computer Science, vol. 132, pp. 937–946 (2018)
Chong, W.Y., Selvaretnam, B., Soon, L.-K.: Natural language processing for sentiment analysis: an exploratory analysis on tweets. In: 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology (IEEE), vol. 43 (2014)
Devi, D.N., Kumar, C.K., Prasad, S.: A feature-based approach for sentiment analysis by using support vector machine. In: 2016 IEEE 6th International Conference on Advanced Computing (IACC), pp. 3–8 (2016)
Alessia, D., Ferri, F., Grifoni, P., Guzzo, T.: Approaches, tools and applications for sentiment analysis implementation. Int. J. Comput. Appl. (0975–8887) 125(3) (2015)
Godsay, M.: The process of sentiment analysis: a study. Int. J. Comput. Appl. (0975–8887) 126(7) (2015)
Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5, 1093–1113 (2014)
Bhanap, S., Kawthekar, S.: Twitter sentiment polarity classification & feature extraction. IOSR J. Comput. Eng. (IOSR-JCE), e-ISSN: 2278-0661, p-ISSN: 2278-8727, pp. 01–03
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347–354 (2005)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-15-0936-0_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0935-3
Online ISBN: 978-981-15-0936-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)