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

P. Tiwari, B. K. Mishra, Sachin Kumar, Vivek Kumar (Ph.D)
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引用次数: 31

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.
n-gram方法在烂番茄评论数据集情感分析中的实现
情感分析旨在获得内容的基本视角,这可能是任何具有主观假设的内容,例如在线审计,博客帖子评论,电影评级等。这些调查和网站可能被分为不同的极端集合,例如,消极的、积极的和无偏的,记住最终目标是集中信息数据集的数据。监督机器学习策略将这些评论分组。本文考虑了三种不同的机器学习计算,例如支持向量机(SVM)、最大熵(ME)和朴素贝叶斯(NB),以安排人类的结论。各种策略的准确性基本上是在考虑最终目标的前提下进行检查,以实现其执行,例如准确性,审查,f-measure和精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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