Unsupervised Fine-Grained Sentiment Analysis System Using Lexicons and Concepts

Conference paper

DOI: 10.1007/978-3-319-12024-9_3

Part of the Communications in Computer and Information Science book series (CCIS, volume 475)
Cite this paper as:
Ofek N., Rokach L. (2014) Unsupervised Fine-Grained Sentiment Analysis System Using Lexicons and Concepts. In: Presutti V. et al. (eds) Semantic Web Evaluation Challenge. SemWebEval 2014. Communications in Computer and Information Science, vol 475. Springer, Cham


Sentiment is mainly analyzed at a document, sentence or aspect level. Document or sentence levels could be too coarse since polar opinions can co-occur even within the same sentence. In aspect level sentiment analysis often opinion-bearing terms can convey polar sentiment in different contexts. Consider the following laptop review: “the big plus was a large screen but having a large battery made me change my mind,” where polar opinions co-occur in the same sentence, and the opinion term that describes the opinion targets (“large”) encodes polar sentiments: a positive for screen, and a negative for battery. To parse these differences, our approach is to identify opinions with respect to the specific opinion targets, while taking the context into account. Moreover, considering that there is a problem of obtaining an annotated training set in each context, our approach uses unlabeled data.


Fine-grained sentiment analysis Opinion mining Lexicon 

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Ben-Gurion University of the NegevBeershebaIsrael

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