Paper: A Rule-Based Approach to Aspect Extraction from Product Reviews

ACL ID W14-5905
Title A Rule-Based Approach to Aspect Extraction from Product Reviews
Venue Workshop on Natural Language Processing for Social Media
Session  
Year 2014
Authors

Sentiment analysis is a rapidly growing research field that has attracted both academia and in- dustry because of the challenging research problems it poses and the potential benefits it can provide in many real life applications. Aspect-based opinion mining, in particular, is one of the fundamental challenges within this research field. In this work, we aim to solve the problem of aspect extraction from product reviews by proposing a novel rule-based approach that exploits common-sense knowledge and sentence dependency trees to detect both explicit and implicit as- pects. Two popular review datasets were used for evaluating the system against state-of-the-art aspect extraction techniques, obtaining higher detection accuracy for both datasets.

@InProceedings{poria-EtAl:2014:SocialNLP,
  author    = {Poria, Soujanya  and  Cambria, Erik  and  Ku, Lun-Wei  and  Gui, Chen  and  Gelbukh, Alexander},
  title     = {A Rule-Based Approach to Aspect Extraction from Product Reviews},
  booktitle = {Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP)},
  month     = {August},
  year      = {2014},
  address   = {Dublin, Ireland},
  publisher = {Association for Computational Linguistics and Dublin City University},
  pages     = {28--37},
  url       = {http://www.aclweb.org/anthology/W14-5905}
}