Pragmatic Analysis of Classification Techniques based on Hyper-parameter Tuning for Sentiment Analysis

EasyChair Preprint 3195

Abstract

The evolution of technology and strong social network has empowered the online user community to share their views on almost every product, event or issue. This has led to a large amount of unstructured online user generated data. Furthermore, every company selling online products analyses its product’s demand and also focuses on their corresponding user reviews. This online user data needs to be analysed for effective decision making either for the user or for the manufacturer. For this, Sentiment Analysis plays a vital role and is extremely useful in social media monitoring as it allows insight of the wider public opinion. In the present study, Amazon product review data set is used to perform sentiment analysis. The proposed model is trained for four different classifiers: Naive Bayes, Support Vector Machine, Logistic Regression, and Random Forest, the model achieved a maximum accuracy of 91% using Logistic Regression. Furthermore, a comparative analysis of various algorithms is also discussed. The study focuses on the importance of hyper parameter tuning while training a classifier which helps in achieving better results than other previous approaches.

Keyphrases: Amazon product reviews, Hyper-parameter tuning, Opinion Mining, Sentiment Analysis, comparative analysis, logistic regression

@booklet{EasyChair:3195,
  author    = {Rohan Ahuja and Kunal Vats and Chirag Pahuja and Tanuj Ahuja and Charu Gupta},
  title     = {Pragmatic Analysis of Classification Techniques based on Hyper-parameter Tuning for Sentiment Analysis},
  howpublished = {EasyChair Preprint 3195},
  year      = {EasyChair, 2020}}