Aspect Extraction and Sentiment Analysis for E-Commerce Product Reviews

  • Enakshi JanaEmail author
  • V. Uma
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 35)


Throughout the globe, with the immense increase of the number of users of the internet and simultaneously the massive expansion of the e-commerce platform, millions of products are sold online, and users are more involved in shopping online. To improve user experience and their satisfaction, online shopping platform enables every user to give their feedback, rating, and review for each and every product that they buy online to help other users. Some popular products on a leading e-commerce platform have of thousands of reviews. Many of those reviews are long and contains only a few sentences which are related to a particular feature of a product. Thus, it becomes really hard for a customer to understand a review and make a decision in buying that product. Manufacturer also need to keep track of customer review regarding the different features of the product to improve the sales of poorly performed one. It becomes very difficult for the user and manufacturer of the product to understand customer view about different features of the product. So, we need accurate opinion-based product review sentiment analysis which will help both customers and product manufacturer to understand and focus on a particular aspect of the product.

This paper proposes the idea of aspect wise product review sentiment analysis. This work explains the methods that can be used for aspect and opinion identification from product reviews. The comparison of different machine learning algorithms used for sentiment analysis of the reviews is also presented. This paper shows that logistic regression with L1 regularization performs best as compared to other algorithms in performing sentiment classification. L1 regularization is good for high dimensional data with multicollinearity among features. This work concludes that text classification with proper regularization is crucial for good accuracy.


Aspect extraction Opinion mining Sentiment analysis Support Vector Machine E-commerce 


  1. 1.
    Liu, Y., Lu, J., Shahbazzade, S.: Sentiment classification of e-commerce product quality reviews by FL-SVM approaches. In: 2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), Berkeley.
  2. 2.
    Kumar, K.L.S., Desai, J., Majumdar, J.: Opinion mining and sentiment analysis on online customer review. In: 2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Chennai, India.
  3. 3.
    Singla, Z., Randhawa, S., Jain, S.: Statistical and sentiment analysis of consumer product reviews. In: 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, India.
  4. 4.
    Hegde, Y., Padma, S.K.: Sentiment analysis using random forest ensemble for mobile product reviews in Kannada. In: 2017 IEEE 7th International Advance Computing Conference (IACC), Hyderabad, India.
  5. 5.
    Lizhen, L., Wei, S., Hanshi, W., Chuchu, L., Jingli, L.: A novel feature-based method for sentiment analysis of Chinese product reviews. China Commun. 11(3) (2014). ISSN 1673-5447CrossRefGoogle Scholar
  6. 6.
    Kumari, U., Sharma, A.K., Soni, D.: Sentiment analysis of smart phone product review using SVM classification technique. In: International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (2017)Google Scholar
  7. 7.
    Wan, Y., Nie, H., Lan, T., Wang, Z.: Fine-grained sentiment analysis of online reviews. In: 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Zhangjiajie, China (2015)Google Scholar
  8. 8.
    Sudhakaran, P., Hariharan, S., Lu, J.: Classifying product reviews from balanced datasets for sentiment analysis and opinion mining. In: 2014 6th International Conference on Multimedia, Computer Graphics and Broadcasting, Haikou, China (2014)Google Scholar
  9. 9.
    Krishna, M.H., Rahamathulla, K., Akbar, A.: A feature based approach for sentiment analysis using SVM and coreference resolution. In: 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India (2017)Google Scholar
  10. 10.
    Devasia, N., Sheik, R.: Feature extracted sentiment analysis of customer product reviews. In: 2016 International Conference on Emerging Technological Trends (ICETT), Kollam, India (2016)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer SciencePondicherry UniversityKalapetIndia

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