Sentence-level emotion mining based on combination of adaptive Meta-level features and sentence syntactic features
Sentence-level emotion mining based on combination of adaptive Meta-level features and sentence syntactic features
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DOI：
10.1016/j.engappai.2017.08.006
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发表时间：
2017-10-01
影响因子：
8
通讯作者：
Hosseini, Akram Sadat
中科院分区：
文献类型：
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作者：
Hosseini, Akram Sadat
如今，由于文本数据的增加，识别文本的情感可以帮助更好地理解上下文。然而，从文本中查找情感信息是一项非常复杂的任务，因为需要自动理解人类句子，这些句子通常是模糊的并且依赖于上下文，而上下文应该以不同的方式解释和表示。可以通过调查该词在大量带注释文本的语料库中出现的频率来推断该词的含义。本文提出了一种从现有词典资源（静态词典）中学习自适应词典的方法，以提高两个数据集的情绪检测任务的性能。自适应词典的学习可以让我们区分静态词典中单词的初始情感和上下文中提到的单词的自适应情感，从而更好地理解单词的情感倾向。此外，本研究提出了一种基于静态和自适应词典和句子句法特征派生的元级特征相结合的情感检测新方法。据我们所知，这是第一项对自动情绪检测的多样化特征集的相对重要性进行全面分析的研究。在 ISEAR 和 Aman 数据集上进行的大量实验表明，学习自适应词典使情感挖掘算法更加准确。 (C) 2017 Elsevier Ltd. 保留所有权利。
Nowadays, because of increasing of text data, recognizing the emotions of text can help to get a better comprehension of context. However, finding emotional information from text is a very complex task because it is needed to automatically understand of human sentences that usually are vague and dependent on context which should be interpreted and represented in different ways. The sense of a word can be inferred by investigating the frequency of occurrence of the word in a large corpus of annotated text. This paper has presented a method for learning adaptive lexicon from existing lexicon resources (static lexicons) to improve performance of emotion detection task for two data sets. The learning of adaptive lexicon would allow us to distinguish between the initial emotion of words in the static lexicons and adaptive emotion of words that is mentioned in context, and we get a better understanding of the emotional orientation of words. Furthermore, this study proposes a novel approach for emotion detection based on the combination of Meta-level features derived from static and adaptive lexicons and sentences syntactic features. To the best of our knowledge, this is the first study that provides a comprehensive analysis of the relative importance of a very diverse feature set for automatic emotion detection. Extensive experiments on ISEAR and Aman data sets show that learning adaptive lexicon enables emotion mining algorithms to be more accurate. (C) 2017 Elsevier Ltd. All rights reserved.