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  1. Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM '12)
  2. Finding emotion in image descriptions
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A Bayesian modeling approach to multi-dimensional sentiment distributions prediction
A unified graph model for Chinese product review summarization using richer information
Transverse subjectivity classification
Retrieval approach to extract opinions about people from resource scarce language news articles
Combining lexicon and learning based approaches for concept-level sentiment analysis
Predicting collective sentiment dynamics from time-series social media
A generic approach to generate opinion lists of phrases for opinion mining applications
Finding emotion in image descriptions
Crowdsourcing recommendations from social sentiment
Fast learning for sentiment analysis on bullying

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Finding emotion in image descriptions

Content Provider ACM Digital Library
Author Ulinski, Morgan Hirschberg, Julia Soto, Victor
Abstract 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.
Starting Page 1
Ending Page 7
Page Count 7
File Format PDF
ISBN 9781450315432
DOI 10.1145/2346676.2346684
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2012-08-12
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Mood classification Image descriptions Text tagging
Content Type Text
Resource Type Article
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