Domain Specific Learning for Sentiment Classification and Activity Recognition

Active Learning for Cross-domain Sentiment Classification

In the literature, various approaches have been proposed to address the domain adaptation problem in sentiment classification (also called cross-domain sentiment classification). However, the adaptation performance normally much suffers when the data distributions in the source and target domains differ significantly. In this paper, we suggest to perform active learning for cross-domain sentime...

Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification

We present a method that learns word embedding for Twitter sentiment classification in this paper. Most existing algorithms for learning continuous word representations typically only model the syntactic context of words but ignore the sentiment of text. This is problematic for sentiment analysis as they usually map words with similar syntactic context but opposite sentiment polarity, such as g...

Learning Domain-specific Sentiment Lexicon with Supervised Sentiment-aware LDA

Domain-Specific Sentiment Classification for Games-Related Tweets

Sentiment classification provides information about the author’s feeling toward a topic through the use of expressive words. However, words indicative of a particular sentiment class can be domain-specific. We train a text classifier for Twitter data related to games using labels inferred from emoticons. Our classifier is able to differentiate between positive and negative sentiment tweets labe...

Finding Domain Specific Polar Words for Sentiment Classification

This paper presents a method of using conditional random fields (CRF) for extracting polar words and determining the overall sentiment of text. We frame sentiment classification as a feature selection problem and conduct three sets of experiments by using: prior polarity lexicons, bag-of-words classifiers and CRF sequence models. The results show the potential of utilizing CRFs in discovering h...