Sentiment Analysis of Product Reviews of Ecommerce Websites

  • Shubhojit SarkarEmail author
  • Souparna Palit
Conference paper
Part of the Algorithms for Intelligent Systems book series (AIS)


While buying a product on an e-commerce website, a consumer has to face the inevitable question whether the product which he is buying is the best option or not. With the exponential growth in the number of e-commerce sites and the products that they are selling, the customer has to identify the most optimally useful product. He has to choose the product that is best fit and gives value for the money. The previous advances used for analyzing whether a product is good or bad uses traditional sentiment analysis methods performing operations on the ratings and reviews submitted by a customer after purchase, whose accuracy lies in the lower half of the graph. Apart from giving comparatively low accuracy percentage, these previous approaches used bipolar classification, which means that the products were classified as good or bad without acknowledging the fact that a product can be average or just okay.


Sentiment analysis Support vector machine Bidirectional associative memory Decision tree Naïve bayes 


  1. 1.
    Liu B (2012) Sentiment analysis and opinion mining, synthesis lectures on human language technologies. Morgan & Claypool Publishers, San RafaelGoogle Scholar
  2. 2.
    Kosko B (1988) Bidirectional associative memories. IEEE Trans Syst Man Cybern 18(1):49–60MathSciNetCrossRefGoogle Scholar
  3. 3.
    Joachims T (1998) Text categorization with support vector machines: learning with many relevant features. Lec Notes Comput Sci 137–142Google Scholar
  4. 4.
    Rish I (2001) An empirical study of the naive Bayes classifier. In: IJCAI workshop on empirical methods in AIGoogle Scholar
  5. 5.
    Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21(3):660–674MathSciNetCrossRefGoogle Scholar
  6. 6.
    Jagdale RS, Shirsat VS, Deshmukh SN (2018) Sentiment analysis on product reviews using machine learning techniques. Adv Intell Syst Comput 639–647Google Scholar
  7. 7.
    Anshuman, Rao S, Kakkar M (2017) A rating approach based on sentiment analysis. In: 7th international conference on cloud computing, data science & engineering—confluence, pp 557–562Google Scholar
  8. 8.
    Umadevi V (2014) Sentiment analysis using weka. Int J Eng Trends Technol (IJETT) 18(4):181–183CrossRefGoogle Scholar
  9. 9.
    Pawlak Z (2003) A rough set view on Bayes’ theorem. Int J Intell Syst 18(5):487–498CrossRefGoogle Scholar
  10. 10.
    Garcia-Gutierrez J, Martínez-Álvarez F, Troncoso A, Riquelme JC (2014) A comparative study of machine learning regression methods on LiDAR data: a case study. In: International joint conference SOCO’13-CISIS’13-ICEUTE’13. Advances in intelligent systems and computing, vol 239. Springer, ChamGoogle Scholar
  11. 11.
    van der Aalst W (2016) Data mining. In: Process mining. Springer, HeidelbergGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Information TechnologySt. Thomas’ College of Engineering and TechnologyKolkataIndia

Personalised recommendations