iTac: Aspect Based Sentiment Analysis using Sentiment Trees and Dictionaries

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 351–355, Dublin, Ireland, August 23-24, 2014. iTac: Aspect Based Sentiment Analysis using Sentiment Trees and Dictionaries Fritjof Bornebusch1, Glaucia Cancino1, Melanie Diepenbeck1, Rolf Drechsler1,2, Smith Djomkam1, Alvine Nzeungang Fanseu1, Maryam Jalali1, Marc Michael1, Jamal Mohsen1, Max Nitze1, Christina Plump1, Mathias Soeken1,2, Fred Tchambo1, Toni1, Henning Ziegler1 1 Faculty of Mathematics and Computer Science, University of Bremen, Germany 2 Cyber-Physical Systems, DFKI GmbH, Bremen, Germany itac@cs.uni-bremen.de Abstract This paper describes our approach for the fourth task of the SemEval 2014 challenge: Aspect Based Sentiment Analysis. Our sys- tem is designed to solve all four subtasks: (i) identifying aspect terms, (ii) determin- ing the polarity of an aspect term, (iii) de- tecting aspect categories, and (iv) determin- ing the polarity of a predefined aspect cate- gory. Our system is based on the Stanford sentiment tree. 1 Introduction Online reviewing, rating, and recommendation have become quite popular nowadays. Based on online reviews and rating, people may decide whether to buy a certain product or visit a certain place (restaurant, shop, etc.). Due to the increasing number of reviews, an automatic system is needed that can evaluate these reviews as positive, negative, or neutral. In this paper, we propose a system for the fourth task of the SemEval 2014 challenge (Aspect Based Sentiment Analysis). The target is to identify as- pects of given target entities and to determine the sentiment that is expressed towards each aspect in terms of a polarity. The problem has been divided into four different subtasks: (i) extracting aspects from a given sentence, (ii) determining the polarity of each aspect, (iii) matching suitable aspect cat- egories and (iv) identifying the polarity of these categories. 2 Related Work There are several different approaches to perform sentiment analysis on a given sentence. Refer- ences Turney (2002) and Pang et al. (2002) started This work is licenced under a Creative Commons Attribu- tion 4.0 International License. Page numbers and proceed- ings footer are added by the organizers. License details: http://creativecommons.org/licenses/by/4.0/ to classify a given sentence to be either positive or negative. Dave et al. (2003) continued to include the neutral semantic orientation to his work. These approaches perform sentiment analysis on a whole sentence and use phrases such as adjectives and adverbs to get a polarity. They collect all these phrases and determine their polarity (e.g. positive, neutral, or negative). Hence, it differs from our work that performs sentiment analysis based on each aspect term. Another approach by Snyder and Barzilay (2007) tries to perform aspect based sentiment analysis, which performs sentiment analysis for various as- pects for a given restaurant. Our work differs from their approach and is more closely related to Hu and Liu (2004). Individual parts of the sentence are classified separately since different parts can express different polarities. But the authors only consider product features instead of aspect terms. Aspect terms can be product features but they can also include conditions such as ambience that in- fluences …

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