UMCC_DLSI: A Probabilistic Automata for Aspect Based Sentiment Analysis

This work introduces a new approach for aspect based sentiment analysis task. Its main purpose is to automatically assign the correct polarity for the aspect term in a phrase. It is a probabilistic automata where each state consists of all the nouns, adjectives, verbs and adverbs found in an annotated corpora. Each one of them contains the number of occurrences in the annotated corpora for the four required polarities (i.e. positive, negative, neutral and conflict). Also, the transitions between states have been taken into account. These values were used to assign the predicted polarity when a pattern was found in a sentence; if a pattern cannot be applied, the probabilities of the polarities between states were computed in order to predict the right polarity. The system achieved results around 66% and 57% of recall for the restaurant and laptop domain respectively.