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Implementation of n-gram Methodology for Rotten Tomatoes Review Dataset Sentiment Analysis

Implementation of n-gram Methodology for Rotten Tomatoes Review Dataset Sentiment Analysis
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Author(s): Prayag Tiwari (National University of Science and Technology MISiS, Department of Computer Science and Engineering, Moscow, Russia), Brojo Kishore Mishra (C. V. Raman College of Engineering, Department of Information Technology, Bhubaneswar, India), Sachin Kumar (Indian Institute of Technology Roorkee, Center for Transportation Systems, Roorkee, India)and Vivek Kumar (National University of Science and Technology MISiS, Department of Computer Science and Engineering, Moscow, Russia)
Copyright: 2020
Pages: 13
Source title: Cognitive Analytics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-2460-2.ch036

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Abstract

Sentiment Analysis intends to get the basic perspective of the content, which may be anything that holds a subjective supposition, for example, an online audit, Comments on Blog posts, film rating and so forth. These surveys and websites might be characterized into various extremity gatherings, for example, negative, positive, and unbiased keeping in mind the end goal to concentrate data from the info dataset. Supervised machine learning strategies group these reviews. In this paper, three distinctive machine learning calculations, for example, Support Vector Machine (SVM), Maximum Entropy (ME) and Naive Bayes (NB), have been considered for the arrangement of human conclusions. The exactness of various strategies is basically inspected keeping in mind the end goal to get to their execution on the premise of parameters, e.g. accuracy, review, f-measure, and precision.

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