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首页> 外文期刊>Arabian Journal for Science and Engineering >RIFT: A Rule Induction Framework for Twitter Sentiment Analysis
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RIFT: A Rule Induction Framework for Twitter Sentiment Analysis

机译：RIFT：Twitter情绪分析的规则归纳框架

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摘要

The rapid evolution of microblogging and the emergence of sites such as Twitter have propelled online communities to flourish by enabling people to create, share and disseminate free-flowing messages and information globally. The exponential growth of product-based user reviews has become an ever-increasing resource playing a key role in emerging Twitter-based sentiment analysis (SA) techniques and applications to collect and analyse customer trends and reviews. Existing studies on supervised black-box sentiment analysis systems do not provide adequate information, regarding rules as to why a certain review was classified to a class or classification. The accuracy in some ways is less than our personal judgement. To address these shortcomings, alternative approaches, such as supervised white-box classification algorithms, need to be developed to improve the classification of Twitter-based microblogs. The purpose of this study was to develop a supervised white-box microblogging SA system to analyse user reviews on certain products using rough set theory (RST)-based rule induction algorithms. RST classifies microblogging reviews of products into positive, negative, or neutral class using different rules extracted from training decision tables using RST-centric rule induction algorithms. The primary focus of this study is also to perform sentiment classification of microblogs (i.e. also known as tweets) of product reviews using conventional, and RST-based rule induction algorithms. The proposed RST-centric rule induction algorithm, namely Learning from Examples Module version: 2, and LEM2 Corpus-based rules (LEM2 CBR),which is an extension of the traditional LEM2 algorithm, are used. Corpus-based rules are generated from tweets, which are unclassified using other conventional LEM2 algorithm rules. Experimental results show the proposed method, when compared with baseline methods, is excellent, with regard to accuracy, coverage and the number of rules employed. The approach using this method achieves an average accuracy of 92.57% and an average coverage of 100%, with an average number of rules of 19.14.
机译：微博的快速发展以及Twitter等网站的出现，通过使人们能够在全球范围内创建，共享和传播自由流动的消息和信息，推动了在线社区的蓬勃发展。基于产品的用户评论的指数增长已成为一种不断增长的资源，在新兴的基于Twitter的情感分析（SA）技术和应用程序中，以收集和分析客户趋势和评论为关键。现有的关于受监督的黑匣子情绪分析系统的研究没有提供足够的信息，有关特定评论为何被归类为类别或分类的规则。在某些方面，准确性低于我们的个人判断。为了解决这些缺点，需要开发替代方法，例如监督白盒分类算法，以改进基于Twitter的微博客的分类。这项研究的目的是开发一个受监督的白盒微博SA系统，以使用基于粗糙集理论（RST）的规则归纳算法分析某些产品上的用户评论。 RST使用以RST为中心的规则归纳算法从训练决策表中提取的不同规则，将产品的微博评论分为正面，负面或中立类别。这项研究的主要重点还在于使用常规的基于RST的规则归纳算法对产品评论的微博（即也称为推文）进行情感分类。提出了以RST为中心的规则归纳算法，即从示例模块版本2中学习，并使用了LEM2基于语料库的规则（LEM2 CBR），它是对传统LEM2算法的扩展。基于语料库的规则是从推文生成的，这些推文未使用其他常规LEM2算法规则进行分类。实验结果表明，与基线方法相比，该方法在准确性，覆盖范围和使用的规则数量方面都非常出色。使用此方法的方法实现了92.57％的平均准确度和100％的平均覆盖率，平均规则数为19.14。

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