Semantic Lexicon Expansion for Concept-Based Aspect-Aware Sentiment Analysis

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 475)


We have developed a prototype for sentiment analysis that is able to identify aspects of an entity being reviewed, along with the sentiment polarity associated to those aspects. Our approach relies on a core ontology of the task, augmented by a workbench for bootstrapping, expanding and maintaining semantic assets that are useful for a number of text analytics tasks. The workbench has the ability to start from classes and instances defined in an ontology and expand their corresponding lexical realizations according to target corpora. In this paper we present results from applying the resulting semantic asset to enhance information extraction techniques for concept-level sentiment analysis. Our prototype(Demo at is able to perform SemSA’s Elementary Task (Polarity Detection), Advanced Task #1 (Aspect-Based Sentiment Analysis), and Advanced Task #3 (Topic Spotting).


Sentiment Analysis Conceptual Drift Polarity Inversion Polarity Detection Positive Sentiment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Thomas J. Watson Research CenterHawthorneUSA
  2. 2.IBM Research AlmadenSan JoseUSA

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