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Opinion mining using ensemble text hidden Markov models for text classification

机译：使用集成文本隐藏马尔可夫模型进行文本分类的观点挖掘

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With the rapid growth of social media, text mining is extensively utilized in practical fields, and opinion mining, also known as sentiment analysis, plays an important role in analyzing opinion and sentiment in texts. Methods in opinion mining generally depend on a sentiment lexicon, which is a set of predefined key words that express sentiment. Opinion mining requires proper sentiment words to be extracted in advance and has difficulty classifying sentences that imply an opinion without using any sentiment key words. This paper presents a new sentiment analysis method, based on text-based hidden Markov models (TextHMMs), for text classification that uses a sequence of words in training texts instead of a predefined sentiment lexicon. We sought to learn text patterns representing sentiment through ensemble TextHMMs. Our method defines hidden variables in TextHMMs by semantic cluster information in consideration of the co-occurrence of words, and thus calculates the sentiment orientation of sentences by fitted TextHMMs. To reflect diverse patterns, we applied an ensemble of TextHMM-based classifiers. In the experiments with a benchmark data set, we show that this method is superior to some existing methods and particularly has potential to classify implicit opinions. We also demonstrate the practicality of the proposed method in a real-life data set of online market reviews. (C) 2017 Elsevier Ltd. All rights reserved.
机译：随着社交媒体的迅速发展，文本挖掘在实际领域中得到了广泛的应用，意见挖掘（也称为情感分析）在分析文本中的意见和情感方面起着重要的作用。意见挖掘中的方法通常取决于情感词典，情感词典是表达情感的一组预定义关键字。观点挖掘要求事先提取适当的情感词，并且很难在不使用任何情感关键词的情况下对隐含观点的句子进行分类。本文提出了一种新的情感分析方法，该方法基于基于文本的隐式马尔可夫模型（TextHMM），用于文本分类，该方法使用训练文本中的单词序列代替预定义的情感词典。我们试图通过整体TextHMM学习表示情感的文本模式。我们的方法考虑到单词的同时出现，通过语义聚类信息在TextHMM中定义了隐藏变量，从而通过拟合的TextHMM计算出句子的情感取向。为了反映各种模式，我们应用了一组基于TextHMM的分类器。在具有基准数据集的实验中，我们表明该方法优于某些现有方法，尤其具有对隐式意见进行分类的潜力。我们还在在线市场评论的真实数据集中证明了该方法的实用性。 （C）2017 Elsevier Ltd.保留所有权利。

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