使用机器学习进行产品评估的Twitter情感分析

N. Yadav, Omkar Kudale, Srishti Gupta, A. Rao, Ajitkumar Shitole
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引用次数: 14

摘要

Twitter是一个微博客网站,是一个巨大的公众意见库,以无数人、产品、公司、商品等的方向表达。情感评价是对一个人的公众评价进行分析的体系。与twitter相结合的情感分析提供了对twitter上表达内容的有益见解。社交媒体上大量可用的在线评估和帖子为团体提供了宝贵的反馈,帮助他们做出更明智的选择,指导他们的营销技术针对用户的消遣和选择。因此,情绪评估对于确定公众对选定服务或产品的意见至关重要。本文强调了根据推文中表达的评论对产品评论(可以在推特的形式内)进行分类所使用的不同技术,以分析海量行为是积极的、消极的还是中性的,并利用该分析对产品市场进行评估。本文中使用的数据是我们从twitter上收集的在线产品评论,并用于对令人满意的分类器进行情感排序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Twitter Sentiment Analysis Using Machine Learning For Product Evaluation
Twitter, a micro-running a blog website, is a massive repository of public opinions expressed in the direction of numerous humans, offerings, companies, merchandise, etc. Sentiment evaluation is the system of analyzing one's public evaluations. Sentiment analysis whilst combined with twitter offers beneficial insights into what's expressed on Twitter. The big availability of online evaluations and postings in social media gives invaluable feedback for groups to make better knowledgeable choices in guidance their marketing techniques towards user's pastimes and alternatives. Sentiment evaluation is, therefore, vital for determining the general public's opinion toward selected services or products. This paper emphasizes the different techniques utilized for classifying the product critiques (which can be within the form of tweets) according to critiques expressed in tweets to analyze whether or not the massive behavior is positive, negative or neutral and use of that analysis for the evaluation of product market. Data used in this look at our online product critiques gathered from twitter and used to rank the satisfactory classifier for sentiments.
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