Abstract
This paper describes our submission to SemEval 2014 Task 41 (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 subtasks (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.
Highlights
In a real-life scenario, we usually do not have any golden aspects at our disposal
We identify aspect term categories with the help of the English WordNet and derive their polarities based on the polarities of individual aspects
This work is related to polarity detection based on a list of evaluative items, i.e. subjectivity lexicons, generally described e.g. in Taboada et al (2011)
Summary
In a real-life scenario, we usually do not have any golden aspects at our disposal. it could be practical to be able to extract both aspects and their polarities at once. We first parse the data, bearing in mind that it is very difficult to detect both sources/targets and their aspects on plain text corpora. This holds especially for pro-drop languages, e.g. Czech (Veselovskaet al., 2014) but the proposed method is still language independent to some extent. We detect the polarity items in the parsed text using a union of two different existing subjectivity lexicons (see Section 2). Afterwards, we extract the aspect terms in the dependency structures containing polarity ex-. In this task, we employ several handcrafted rules detecting aspects based on syntactic features of the evaluative sentences, inspired by the method by Qiu et al (2011).
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