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Comparison of Probabilistic Corpus Based method and Vector Space Model for Emotion Recognition from Poems

机译:基于概率语料库的诗歌情感识别方法和向量空间模型的比较

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This paper discusses the automatic detection of emotions in English poems. Emotions are classified based on ?Navarasa? which is described in ?Natyasastra?. Navarasa consists of nine basic emotions such as Love, Sad, Anger, Hate, Fear, Surprise, Courage, Joy and Peace. We have manually created an emotions tagged corpus from poems. Using poems mined from the web, we applied corpus-based tagging method to recognize the emotion of a poem. For the emotion recognition, we have used the Vector Space Model with a total of 348 poems of 165 poets mined from the web. We have approached this problem from four perspectives.Traditional Vector Space model Vector space model, eliminating stop words (emotionless words) and without stemming Vector Space model without eliminating stop words and without stemming. Corpus-Based emotion recognition system. The Traditional Vector Space model gives better performance than other methods.
机译:本文讨论了英语诗歌中情感的自动检测。情绪是根据“ Navarasa”分类的在“ Natyasastra”中有描述。 Navarasa包含九种基本情感,例如爱,悲伤,愤怒,仇恨,恐惧,惊奇,勇气,喜悦和和平。我们从诗歌中手动创建了一个带有情感标记的语料库。使用从网络上提取的诗歌,我们应用了基于语料库的标记方法来识别诗歌的情感。为了进行情感识别,我们将向量空间模型与从网络上挖掘的165名诗人的348诗一起使用。我们从四个角度解决了这个问题。传统向量空间模型向量空间模型,消除了停用词(不动词),并且没有词干向量空间模型没有消除了停用词并且没有词干。基于语料库的情绪识别系统。传统向量空间模型比其他方法具有更好的性能。

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