Using appraisal groups for sentiment analysis

Little work to date in sentiment analysis (classifying texts by ‘positive’ or ‘negative’ orientation) has attempted to use fine-grained semantic distinctions in features used for classification. We present a new method for sentiment classification based on extracting and analyzing appraisal groups such as “very good” or “not terribly funny”. An appraisal group is represented as a set of attribute values in several task-independent semantic taxonomies, based on Appraisal Theory. Semi-automated methods were used to build a lexicon of appraising adjectives and their modifiers. We classify movie reviews using features based upon these taxonomies combined with standard “bag-of-words” features, and report state-of-the-art accuracy of 90.2%. In addition, we find that some types of appraisal appear to be more significant for sentiment classification than others. Categories and Subject Descriptors I.2.7 [Artificial Intelligence]: Natural Language Processing—Text a...

• Type: Conference
• Year: 2005
• Where: CIKM
• Authors: Casey Whitelaw, Navendu Garg, Shlomo Argamon
• Keywords: Appraisal Group, CIKM 2005, Sentiment Analysis, Sentiment Classification