• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of synthetic emotions >A Comparative Study of Different Classification Techniques for Sentiment Analysis
【24h】

A Comparative Study of Different Classification Techniques for Sentiment Analysis

机译：情感分析不同分类技术的比较研究

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Sentiment analysis denotes the analysis of emotions and opinions from text. The authors also refer to sentiment analysis as opinion mining. It finds and justifies the sentiment of the person with respect to a given source of content. Social media contain vast amounts of the sentiment data in the form of product reviews, tweets, blogs, and updates on the statuses, posts, etc. Sentiment analysis of this largely generated data is very useful to express the opinion of the mass in terms of product reviews. This work is proposing a highly accurate model of sentiment analysis for reviews of products, movies, and restaurants from Amazon, IMDB, and Yelp, respectively. With the help of classifiers such as logistic regression, support vector machine, and decision tree, the authors can classify these reviews as positive or negative with higher accuracy values.
机译：情感分析是指对文本中的情感和观点进行的分析。作者还将情感分析称为观点挖掘。它针对给定的内容源找到并证明了该人的情感。社交媒体以产品评论，推文，博客以及状态，帖子等更新的形式包含了大量的情感数据。对这些大量生成的数据的情感分析对于表达大众的观点非常有用。产品评论。这项工作提出了一种高度准确的情感分析模型，用于分别评论来自Amazon，IMDB和Yelp的产品，电影和餐厅。借助逻辑回归，支持向量机和决策树等分类器，作者可以将这些评论分类为具有较高准确度值的正面或负面评论。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号