WordNet: a lexical database for English
Communications of the ACM
WordsEye: an automatic text-to-scene conversion system
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to identify emotions in text
Proceedings of the 2008 ACM symposium on Applied computing
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Word to sentence level emotion tagging for Bengali blogs
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
How can you say such things?!?: recognizing disagreement in informal political argument
LSM '11 Proceedings of the Workshop on Languages in Social Media
What pushes their buttons?: predicting comment polarity from the content of political blog posts
LSM '11 Proceedings of the Workshop on Languages in Social Media
Sentiment analysis of Twitter data
LSM '11 Proceedings of the Workshop on Languages in Social Media
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In this paper, we approach the problem of classifying emotion in image descriptions. A method is proposed to perform 6-way emotion classification and is tested against two labeled datasets: a corpus of blog posts mined from LiveJournal and a corpus of descriptive texts of computer generated scenes. We perform feature selection using the mRMR technique and then use a multi-class linear predictor to classify posts among the Ekman Big Six emotions (happiness, sadness, anger, surprise, fear, and disgust) [9]. We find that TFIDF scores on lexical features and LIWC scores are much more helpful in emotion classification than using scores calculated from existing sentiment dictionaries, and that our proposed method performs significantly better than a baseline classifier that chooses the majority class. On the blog posts, we achieve 40% accuracy, and on the corpus of image descriptions, we achieve up to 63% accuracy.