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Automatic Identification and Recognition of Sentiment Words Using an Optimization‐Based Model with Propagation
Author(s) -
Luo KunHu,
Deng ZhiHong,
Yu HongLiang,
Li ShiYingXue
Publication year - 2015
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21707
Subject(s) - robustness (evolution) , computer science , exploit , artificial intelligence , word (group theory) , sentiment analysis , identification (biology) , set (abstract data type) , optimization algorithm , machine learning , pattern recognition (psychology) , data mining , mathematics , mathematical optimization , biochemistry , chemistry , botany , geometry , computer security , biology , gene , programming language
Sentiment word identification, or SWI, is one of the most basic and important techniques in sentiment analysis. Many existing methods depend on the seed word, and such dependence leads to low robustness. In this paper, we propose a novel method utilizing propagation and optimization model, PRopagation‐based Constrained Optimization Model (PR‐COM) for SWI. Unlike the previous research, we exploit an iterative algorithm to expand the seed word set from the candidate word set, which brings higher robustness. Experimental results on several data sets show that our PR‐COM method is effective and outperforms the state‐of‐art methods.