A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
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Recognizing contextual polarity in phrase-level sentiment analysis
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Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinion
Characterizing debate performance via aggregated twitter sentiment
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Sentiment classification using word sub-sequences and dependency sub-trees
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MemeTube: a sentiment-based audiovisual system for analyzing and displaying microblog messages
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From bias to opinion: a transfer-learning approach to real-time sentiment analysis
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Sentiment analysis of Twitter data
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Survey on mining subjective data on the web
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Generating event storylines from microblogs
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TwiSent: a multistage system for analyzing sentiment in twitter
Proceedings of the 21st ACM international conference on Information and knowledge management
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A Multi-Classifier System for Sentiment Analysis and Opinion Mining
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A joint prediction model for multiple emotions analysis in sentences
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Using the length of the speech to measure the opinion
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User-defined hot topic detection in microblogging
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Emotion detection in suicide notes
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Expert Systems with Applications: An International Journal
A method of feature selection and sentiment similarity for Chinese micro-blogs
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Comparing and combining sentiment analysis methods
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Polarity analysis of micro reviews in foursquare
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ACM Transactions on the Web (TWEB)
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Microblogs as a new textual domain offer a unique proposition for sentiment analysis. Their short document length suggests any sentiment they contain is compact and explicit. However, this short length coupled with their noisy nature can pose difficulties for standard machine learning document representations. In this work we examine the hypothesis that it is easier to classify the sentiment in these short form documents than in longer form documents. Surprisingly, we find classifying sentiment in microblogs easier than in blogs and make a number of observations pertaining to the challenge of supervised learning for sentiment analysis in microblogs.