This paper presents initial results in sentiment analysis clas- sication, as an attempt to go beyond categorizing texts only by 'positive' or 'negative' orientation, using ne-grained features for this purpose. We present a method for sentiment classication based on a concise represen- tation built from analyzing appraisal groups such as \very good" or \not terrible". An appraisal group is represented as a set of attribute values anteceding an appraisal word (adjective). An appraisal lexicon is used to identify adjectives guiding the analysis. We performed experiments classifying movie reviews in Spanish using features based upon attitude taxonomy information, and report improvements on precision with eight dimensional vectors and a support vector machine algorithm.
['Victor M. Morales', 'Saúl León', 'Maya Carrillo', 'Aurelio López-López', 'Luis Enrique Colmenares Guillén']
A Concise Representation for Detailed Sentiment Analysis