基于用户意见的情感分析研究综述

Amber Haroon, Toqeer Mahmood, Rehan Ashraf, Muhammad Asif, S. Naseem, Abdul Wahab Khan
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引用次数: 1

摘要

在这个现代时代,网上购物得到了很多关注。不同的社交媒体平台上有成千上万的顾客评论,这使得用户很难做出购买决定。为了更好地理解用户的意见,进行了情感分析(也称为意见挖掘),这对用户的购买决策产生了重大影响。意见挖掘是根据实体、情感和文本关系来定义的。电子商务网站或社交媒体应用程序上的用户意见对产品利益相关者产生巨大影响。在过去的几十年里,研究人员、公共部门和服务行业都在进行民意挖掘,以根除和审查社会的情绪和意见。本文介绍了最近通过机器学习技术(专注于监督学习、半监督学习、强化学习和无监督学习)、深度学习技术(专注于CNN、RNN和LSTM)进行的基于用户意见的情感分析研究的概况,并提供了背景知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comprehensive Survey of Sentiment Analysis Based on User Opinion
In this modern era online shopping is getting a lot of attention. Thousands of reviews are available from the customers on different social media platforms which makes it difficult for the user to make a purchasing decision. For a better understanding of user opinion, sentiment analysis (also known as opinion mining) has been conducted which makes a major effect on the purchasing decision of the user. Opinion mining is defined in terms of entities, emotions, and textual relationships. User opinions on e-commerce websites or social media apps have a huge impact on product stakeholders. Over the past decades, researchers, the public sector, and the service industry are carrying out opinion mining, to eradicate and examine community sentiments and opinions. This paper presents a survey of recent studies conducted for sentiment analysis based on user opinion through machine learning techniques (focusing on supervised, semi-supervised, reinforcement, and unsupervised learning), deep learning techniques (focusing on CNN, RNN, and LSTM), and provide the background knowledge.
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