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.

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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

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