使用Twitter数据的单词对齐模型进行在线评论分析

T. Lavanya, J. C. Pamila, K. Veningston
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引用次数: 5

摘要

意见在几乎所有的人类活动中都是至关重要的,是影响网络用户行为的关键因素。在这方面,当我们需要做决定时,我们经常会征求别人的意见。意见及其相关概念如情感和情绪是情感分析和意见挖掘的研究对象。从在线评论中挖掘意见的开始与网络上的社交媒体相吻合,例如评论、论坛、博客、微博、Twitter和社交网络。由于大量的自以为是的数据以数字形式产生和可用,因此挖掘这些数据并找到意见或情绪以获得对主题(即产品,政治,体育,教育改革等)的总体意见变得至关重要。在这个方向上,我们提出了一种算法,利用从Twitter中提取的在线评论的词对齐模型来提取意见目标和意见词。意见目标定义为用户表达意见的主题。意见词是用来表达用户意见的词。该项目的目的是确定博客或评论作者对某些主题的想法或使用单词对齐模型的在线评论的整体上下文极性。本项目的主要目标是设计一种算法,通过挖掘社交网站即Twitter上发布的用户评论,预测意见词和意见目标,分析产品的市场状况。为了进行评估,使用Twitter API提取与产品评论相关的基准客户评论数据集和真实tweets数据集。结果用Precision和Recall来衡量发现意见词和目标的准确性,这是意见挖掘和情感分析的重要组成部分。实验结果表明,该方法有效地提高了识别精度。此外,这个项目的最终结果是选择设计潜在的面向消费者的产品,如手机,笔记本电脑等。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online review analytics using word alignment model on Twitter data
Opinions are vital in almost all human activities and are key influencers of online user's behavior. In this regard, when we need to make a decision, we often search for the opinions of others. Opinions and its related concepts such as sentiments and emotions are the subjects of study of sentiment analysis and opinion mining. The inception of mining opinions from online reviews coincide with the social media on the Web, e.g., reviews, discussion forums, blogs, micro blogs, Twitter, and social networks. Due to the large volume of opinionated data generated and available in digital forms, it become essential to mine such data and find opinions or sentiments in order to obtain the overall opinion on the topic i.e. products, politics, sports, education reforms, and so on. In this direction, it is proposed to design an algorithm which extracts opinion targets and opinion words using word alignment model for online reviews extracted from Twitter. An opinion target is defined as the topic about which users express their opinions. An opinion words are defined as the words that are used to express users' opinions. The aim of the project is to determine the thoughts of blog or review writer with respect to some topic or the overall contextual polarity of online reviews using word alignment model. The key objective of this project is to design an algorithm that predict opinion words and opinion targets for analyzing the market status of a product by mining user reviews posted in social networking site namely the Twitter. For evaluation, Benchmark Customer Review Dataset and real tweets dataset pertaining to product reviews extracted using Twitter API. The results are measured in terms of Precision and Recall for accuracy of finding opinion words and targets in order to be an essential ingredient for opinion mining and sentiment analysis. The experimental results show that the proposed method achieves better accuracy in an efficient way. In addition, the ultimate outcome of this project to make choice of designing potential consumer oriented products e.g. Mobile, laptop, and so on.
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