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Care more about customers: Unsupervised domain-independent aspect detection for sentiment analysis of customer reviews

机译：更关心客户：无监督的与领域无关的方面检测，可用于客户评论的情绪分析

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摘要

With the rapid growth of user-generated content on the internet, automatic sentiment analysis of online customer reviews has become a hot research topic recently, but due to variety and wide range of products and services being reviewed on the internet, the supervised and domain-specific models are often not practical. As the number of reviews expands, it is essential to develop an efficient sentiment analysis model that is capable of extracting product aspects and determining the sentiments for these aspects. In this paper, we propose a novel unsupervised and domain-independent model for detecting explicit and implicit aspects in reviews for sentiment analysis. In the model, first a generalized method is proposed to learn multi-word aspects and then a set of heuristic rules is employed to take into account the influence of an opinion word on detecting the aspect. Second a new metric based on mutual information and aspect frequency is proposed to score aspects with a new bootstrapping iterative algorithm. The presented bootstrapping algorithm works with an unsupervised seed set. Third, two pruning methods based on the relations between aspects in reviews are presented to remove incorrect aspects. Finally the model employs an approach which uses explicit aspects and opinion words to identify implicit aspects. Utilizing extracted polarity lexicon, the approach maps each opinion word in the lexicon to the set of pre-extracted explicit aspects with a co-occurrence metric. The proposed model was evaluated on a collection of English product review datasets. The model does not require any labeled training data and it can be easily applied to other languages or other domains such as movie reviews. Experimental results show considerable improvements of our model over conventional techniques including unsupervised and supervised approaches.
机译：随着互联网上用户生成内容的快速增长，在线客户评论的自动情绪分析已成为最近的热门研究话题，但是由于互联网上评论的产品和服务种类繁多，范围广泛，特定的模型通常不实用。随着评论数量的增加，开发有效的情感分析模型非常重要，该模型能够提取产品方面并确定这些方面的观点。在本文中，我们提出了一种新颖的无监督且领域无关的模型，用于检测评论中的显式和隐式方面，以进行情感分析。在该模型中，首先提出一种通用的方法来学习多词方面，然后采用一套启发式规则来考虑意见词对方面的影响。其次，提出了一种基于互信息和方面频率的新度量，以一种新的自举迭代算法对方面进行评分。提出的自举算法适用于无监督的种子集。第三，提出了两种基于评论方面之间关系的修剪方法，以消除不正确的方面。最后，模型采用一种方法，该方法使用显式方面和意见词来识别隐式方面。利用提取的极性词典，该方法将词典中的每个意见词映射到具有共现度量的一组预提取的显式方面。提议的模型是在英语产品评论数据集上进行评估的。该模型不需要任何标记的训练数据，并且可以轻松地应用于其他语言或其他领域，例如电影评论。实验结果表明，与传统技术（包括无监督和有监督的方法）相比，我们的模型有了很大的改进。

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  • 来源
    《Knowledge-Based Systems》 |2013年第11期|201-213|共13页
  • 作者

    Ayoub Bagheri; Mohamad Saraee; Franciska de Jong;

  • 作者单位

    Intelligent Database, Data Mining and Bioinformatics Lab, Electrical and Computer Engineering Department, Isfahan University of Technology, Isfahan, Iran;

    School of Computing, Science and Engineering, University of Salford, Manchester, UK;

    Human Media Interaction Group, University of Twente, Enschede. Eramus Universiteit Rotterdam, Erasmus Studio, P.O. Box 1738, 3000 DR Rotterdam, The Netherlands;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Aspect detection; Opinion mining; Review mining; Sentiment analysis; Implicit aspect;

    机译：方面检测;意见挖掘;审查采矿;情绪分析;隐含方面;

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