ANALYSIS OF SENTIMENTAL BIAS THE IMPLEMENTATION OF SUPERVISED MACHINE LEARNING ALGORITHMS

Authors

  • S. Suman Rajest Professor, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India
  • R. Regin Assistant Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, India
  • Shynu T Master of Engineering, Department of Biomedical Engineering, Agni College of Technology, Chennai, Tamil Nadu, India
  • Steffi. R Assistant Professor, Department of Electronics and Communication, Vins Christian College of Engineering, Tamil Nadu, India

Keywords:

Amazon Web Server, Machine Learning, Support Vector Machine, Data Frame, Numpy, Random Forest

Abstract

More and more people are writing reviews of items and services online as a result of the explosion of internet shopping. Text mining is a method for discovering useful patterns in massive datasets. In order to construct novel realities or ideas to be explored further by more conventional experimental methods, a crucial component is used to interface the extracted data. Sentiment analysis presents several obstacles. When people use a computer browser to go online and purchase goods or services, they are engaging in online shopping, a type of electronic commerce. Those looking to make a purchase in the near future can benefit much from reading evaluations of products on the internet. As a result, many opinion mining strategies have been put forward, with one of their main obstacles being the assessment of the direction of a review phrase, whether positive or negative. When it comes to overcoming issues with sentiment classification, machine learning has recently shown to be a useful method. There is no need for human intervention when training a machine learning model; the programme will automatically learn a functional representation. Our proposed supervised machine learning approach, on the other hand, uses widely known ratings as weak supervision signals to classify the sentiment of product reviews. We build a dataset with 15,000 labelled review sentences and 200,000 weakly labelled review sentences from Amazon to test the suggested approach. Superior precision as measured experimentally as contrasted with the prior iteration.

Downloads

Published

2024-01-11

How to Cite

S. Suman Rajest, R. Regin, Shynu T, & Steffi. R. (2024). ANALYSIS OF SENTIMENTAL BIAS THE IMPLEMENTATION OF SUPERVISED MACHINE LEARNING ALGORITHMS. International Journal of Innovative Analyses and Emerging Technology, 4(1), 8–33. Retrieved from https://oajournals.net/index.php/ijiaet/article/view/2547

Issue

Section

Articles

Similar Articles

<< < 1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.