Human Behavior Prediction based on Opinions using Machine Learning Techniques
Sanjay K S1, Ajit Danti2

1Sanjay K S, Benedictine Academy, Bangalore, Karnataka, India.
2Ajit Danti, Christ (Deemed to be University),Bangalore, Karnataka, India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3117-3120 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8733038620/2020©BEIESP | DOI: 10.35940/ijrte.F8733.038620

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Abstract: Prediction is the way of identifying the behavior of a person towards online shopping by analyzing the reviews publicly available on the web. In the present study, machine learning approaches are used to extract reviews from the web and segregate and classify them in to five categories, namely, strongly positive, positive, neutral, negative, and strongly negative, for the prediction of human behavior. Several pre-processing methods (including stop-word removal) are applied and web crawler is used to gather the data. This is followed by the application of Stanford POS tagger for tagging the reviews, which is done after stemming by using the porter stemmer algorithm. Analysis of a person’s behavior is performed and experimental results are compared with machine learning approaches.
Keywords: Behavior, Prediction, Porter Stemmer, POS tagging, Classification.
Scope of the Article: Health Monitoring and Life Prediction of Structures.