Selecting clause emotion for sentence emotion recognition
Selecting clause emotion for sentence emotion recognition
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DOI：
10.1109/nlpke.2011.6138193
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发表时间：
2011-11
期刊：
影响因子：
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通讯作者：
Changqin Quan;F. Ren
中科院分区：
文献类型：
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作者：
Changqin Quan;F. Ren
句子情感识别允许对文本情感进行更深入的分析。基于该句子中的一些分句可以表达句子的情感焦点，本工作提出选择分句情感进行句子情感识别。在第一步中，我们建立了一个最大熵（MaxEnt）分类模型用于单词情感识别。第二步，采用齐次马尔可夫模型（HMM）分类方法对子句进行情感分类。第三步，选择9个文本特征，并使用遗传算法指定每个文本特征的权重。句子情感是这个句子中所有选定的分句状态的附加。实验结果表明，与基线相比，这两个任务分别提高了9.1%和3.6%。研究表明，小句选择能够显著提高句子情感识别的性能。
Sentence emotion recognition allows for deeper analysis of textual emotion. Based on the finding that sentence emotional focus can be expressed by some clauses in this sentence, this work proposes to select clause emotion for sentence emotion recognition. In the first step, a Maximum entropy (MaxEnt) classification model has been built for word emotion recognition. In the second step, a homogeneous Markov model (HMM) classification method is used for clause emotion classification. In the third step, nine text features are selected, and genetic algorithm (GA) is used to specify the weight of each text feature. The sentence emotion is an addition of all selected clause states in this sentence. The experimental results showed that there are 9.1% and 3.6% improvement for the two tasks respectively when comparing with the baseline. It is demonstrated that clause selection is able to improve the performance of sentence emotion recognition significantly.