Abstract

This paper describes our contribution in Opinion Target Extraction OTE and Sentiment Polarity sub tasks of SemEval 2015 ABSA task. A CRF model with IOB notation has been adopted for OTE with several groups of features including syntactic, lexical, semantic, sentiment lexicon features. Our submission for OTE is ranked fifth over twenty submissions. A Logistic Regression model with a weighting schema of positive and negative labels have been used for sentiment polarity; several groups of features (lexical, syntactic, semantic, lexicon and Z score) are extracted. Our submission for Sentiment Polarity is ranked third over ten submissions on the restaurant data set, third over thirteen on the laptops data set, but the first over eleven on the hotel data set that is out-of-domain set.

Highlights

  • Many levels of granularity have been distinguished: Document Level Sentiment Analysis (SA) considers the whole document is about an entity and classifies whether the expressed sentiment is positive, negative or neutral; Sentence Level SA determines the sentiment of each sentence, some papers have focused on Clause Level SA, but they are still not enough; Entity or Aspect-Based SA performs finer-grained analysis in which all entities and their aspects should be extracted and the sentiment towards them should be determined

  • We focus on Opinion Target Extraction (OTE) and Sentiment Polarity towards a target or a category

  • In addition to the restaurant data set presented in tabel 1, two other data sets statistics are presented in table 3 (Laptops data which consists of training and testing data sets while the Hotel test set is out of domain set that was provided to test our model on new domain without having training data)

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Summary

Introduction

Sentiment Analysis (SA) has become more and more interesting since the year 2000, many techniques in Natural Language Processing have been used to understand the expressed sentiment on an entity. We could define the opinion by the quintuple (Liu, 2012) (ei, aij, sijkl, hk, tl) where ei is the entity i, aij are the aspects of the entity i, sijkl is the expressed sentiment on the aspect at the time tl , hk the holder which created the document or the text This definition does not take into account that the entity has aspects that could have other aspects which leads to an aspect hierarchy, in order to avoid this information loss, few work has handled this issue, they proposed to represent the aspect as a tree of aspect terms.

Related Work
Experiments
Sentiment Polarity
Findings
Conclusion and future work
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