UFAL: Using Hand-crafted Rules in Aspect Based Sentiment Analysis on Parsed Data

摘要

This paper describes our submission to SemEval 2014 Task 4 (aspect based sentiment analysis). The current work is based on the assumption that it could be advantageous to connect the subtasks into one workflow, not necessarily following their given order. We took part in all four sub-tasks (aspect term extraction, aspect term polarity, aspect category detection, aspect category polarity), using polarity items detection via various subjectivity lexicons and employing a rule-based system applied on dependency data. To determine aspect categories, we simply look up their WordNet hypernyms. For such a basic method using no machine learning techniques, we consider the results rather satisfactory.