文章基本信息

标题：Word and Sentence Level Emotion Analyzation in Telugu Blog and News
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作者：Sadanandam. Manchala ; D. Chandra Mohan ; A. Nagesh 等
期刊名称：International Journal of Computer Science, Engineering and Applications (IJCSEA)
印刷版ISSN：2231-0088
电子版ISSN：2230-9616
出版年度：2012
卷号：2
期号：3
DOI：10.5121/ijcsea.2012.2317
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Emotion analysis, a recent sub discipline at the crossroads of information retrieval and computational linguistics is becoming increasingly important from application viewpoints of affective computing.Emotion is crucial to identify as it is not open to any objective observation or verification. In this paper, emotion analysis on blog texts has been carried out for a less privileged language, Telugu and the same system has been applied on the English SemEval 2007 affect sensing corpus containing only news headlines. A set of six emotion tags, namely, happy ( ), sad ( ), anger ( ), fear ( ), surprise ( )and disgust ( ), have been selected towards this emotion detection task for reliable and semi-automatic annotation of blog and news data. Conditional Random Field (CRF) based classifier has been applied for recognizing six basic emotion tags for different words of a sentence. The classifier accuracy has been improved by arranging an equal distribution of emotional tags and non-emotional tag. A score based technique has been adopted to calculate and assign tag weights to each of the six emotion tags. A sense based scoring strategy has been applied to identify sentence level emotion scores for the six emotion tags based on the acquired word level emotion tags. Sentence level emotion tagging has been carried out based on the maximum obtained sentence level emotion scores. Evaluation has been conducted for each emotion class separately on 200 test sentences from each of the Telugu blog and English news data. The system has resulted accuracies of 69.82% and 71.06% for happy, 70.24% and 66.42% for sad, 65.73% and 64.27% for anger, 76.01% and 69.90% for disgust, 72.19% and 73.59% for fear and 70.54% and 66.64% for surprise emotion classes on blog and news test data respectively.
关键词：SentiWordNet; Blog; News; Emotion; CRF; Emotion tag; happy; sad; anger; fear; disgust; surprise.