UBham: Lexical Resources and Dependency Parsing for Aspect-Based Sentiment Analysis

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 683–687, Dublin, Ireland, August 23-24, 2014. UBham: Lexical Resources and Dependency Parsing for Aspect-Based Sentiment Analysis Viktor Pekar School of Computer Science University of Birmingham Birmingham, UK v.pekar@cs.bham.ac.uk Naveed Afzal FCIT, North Branch King Abdulaziz University Jeddah, KSA nafzal@kau.edu.sa Bernd Bohnet School of Computer Science University of Birmingham Birmingham, UK b.bohnet@cs.bham.ac.uk Abstract This paper describes the system devel- oped by the UBham team for the SemEval- 2014 Aspect-Based Sentiment Analysis task (Task 4). We present an approach based on deep linguistic processing tech- niques and resources, and explore the pa- rameter space of these techniques applied to the different stages in this task and ex- amine possibilities to exploit interdepen- dencies between them. 1 Introduction Aspect-Based Sentiment Analysis (ASBA) is con- cerned with detection of the author’s sentiment to- wards different issues discussed in a document, such as aspects or features of a product in a cus- tomer review. The specific ASBA scenario we ad- dress in this paper is as follows. Given a sentence from a review, identify (1) aspect terms, specific words or multiword expressions denoting aspects of the product; (2) aspect categories, categories of issues being commented on; (3) aspect term po- larity, the polarity of the sentiment associated with each aspect term; and (4) aspect category polarity, the polarity associated with each aspect category found in the sentence. For example, in: I liked the service and the staff, but not the food. aspect terms are service, staff and food, where the first two are evaluated positively and the last one negatively; and aspect categories are SERVICE and FOOD, where the former is associated with pos- itive sentiment and the latter with negative. It should be noted that a given sentence may contain This work is licenced under a Creative Commons At- tribution 4.0 International Licence. Page numbers and pro- ceedings footer are added by the organisers. Licence details: http://creativecommons.org/licenses/by/4.0/ The research was partially supported by FP7 ICT project “Workbench for Interactive Contrastive Analysis of Patent Documentation” under grant no. FP7-SME-606163. one, several, or no aspect terms, one, several, or no aspect categories, and may express either positive, negative, neutral, or conflicted sentiment. While the ASBA task is usually studied in the context of documents (e.g., online reviews), pecu- liarities of this scenario are short input texts, com- plex categorization schemas, and a limited amount of annotated data. Therefore we focused on ways to exploit deep linguistic processing techniques, which we use for both creating complex classifi- cation features and rule-based processing. 2 Related Work 2.1 Aspect Term Extraction To recognize terms that express key notions in a product or service review, a common general ap- proach has been to extract nouns and noun phrases as potential terms and then apply a certain filtering technique to ensure only the most relevant terms remain. These techniques include statistical asso- ciation tests (Yi et al., 2003), associative mining rules with additional rule-based post-processing steps (Hu and Liu, …

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