Sentence-Level Subjectivity Detection Using Neuro-Fuzzy Models

In this work, we attempt to detect sentencelevel subjectivity by means of two supervised machine learning approaches: a Fuzzy Control System and Adaptive Neuro-Fuzzy Inference System. Even though these methods are popular in pattern recognition, they have not been thoroughly investigated for subjectivity analysis. We present a novel “Pruned ICF Weighting Coefficient,” which improves the accuracy for subjectivity detection. Our feature extraction algorithm calculates a feature vector based on the statistical occurrences of words in a corpus without any lexical knowledge. For this reason, these machine learning models can be applied to any language; i.e., there is no lexical, grammatical, syntactical analysis used in the classification process.

[1]  Bing Liu,et al.  Sentiment Analysis and Opinion Mining , 2012, Synthesis Lectures on Human Language Technologies.

[2]  Rada Mihalcea,et al.  Multilingual Subjectivity: Are More Languages Better? , 2010, COLING.

[3]  Yulan He,et al.  Sentence Subjectivity Detection with Weakly-Supervised Learning , 2011, IJCNLP.

[4]  Rob Law,et al.  Automatic Detection of Subjective Sentences Based on Chinese Subjective Patterns , 2009 .

[5]  Yulan He.  Bayesian Models for Sentence-Level Subjectivity Detection , 2010 .

[6]  Soo-Min Kim,et al.  Automatic Detection of Opinion Bearing Words and Sentences , 2005, IJCNLP.

[7]  Robert Remus.  Improving Sentence-level Subjectivity Classification through Readability Measurement , 2011, NODALIDA.

[8]  Junlan Feng,et al.  Robust Sentiment Detection on Twitter from Biased and Noisy Data , 2010, COLING.

[9]  Timothy W. Finin,et al.  Delta TFIDF: An Improved Feature Space for Sentiment Analysis , 2009, ICWSM.

[10]  Farah Benamara,et al.  Towards Context-Based Subjectivity Analysis , 2011, IJCNLP.

[11]  Bo Pang,et al.  A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts , 2004, ACL.

[12]  Giuseppe Carenini,et al.  Predicting Subjectivity in Multimodal Conversations , 2009, EMNLP.

[13]  Gregory Grefenstette,et al.  Validating the Coverage of Lexical Resources for Affect Analysis and Automatically Classifying New Words along Semantic Axes , 2006, Computing Attitude and Affect in Text.

[14]  Muhammad Abdul-Mageed,et al.  Subjectivity and Sentiment Analysis of Modern Standard Arabic , 2011, ACL.

[15]  Robert Fullér,et al.  Neural Fuzzy Systems , 1995 .

[16]  Rada Mihalcea,et al.  Learning Multilingual Subjective Language via Cross-Lingual Projections , 2007, ACL.

[17]  S. Rustamov,et al.  Human-computer dialogue understanding hybrid system , 2012, 2012 International Symposium on Innovations in Intelligent Systems and Applications.

[18]  Theresa Wilson,et al.  Multimodal Subjectivity Analysis of Multiparty Conversation , 2008, EMNLP.

[19]  John Carroll,et al.  Unsupervised Classification of Sentiment and Objectivity in Chinese Text , 2008, IJCNLP.

[20]  Ellen Riloff,et al.  Learning Extraction Patterns for Subjective Expressions , 2003, EMNLP.

[21]  Vasudeva Varma,et al.  Language Independent Sentence-Level Subjectivity Analysis with Feature Selection , 2012, PACLIC.

[22]  Rada Mihalcea,et al.  Multilingual Subjectivity Analysis Using Machine Translation , 2008, EMNLP.

[23]  Ellen Riloff,et al.  Creating Subjective and Objective Sentence Classifiers from Unannotated Texts , 2005, CICLing.

[24]  Rohini K. Srihari,et al.  A Vector Space Model for Subjectivity Classification in Urdu aided by Co-Training , 2010, COLING.

[25]  Janyce Wiebe,et al.  Just How Mad Are You? Finding Strong and Weak Opinion Clauses , 2004, AAAI.

[26]  Mark A. Clements,et al.  Sentiment analysis using Neuro-Fuzzy and Hidden Markov models of text , 2013, 2013 Proceedings of IEEE Southeastcon.

[27]  Sabine Bergler,et al.  Mining WordNet for a Fuzzy Sentiment: Sentiment Tag Extraction from WordNet Glosses , 2006, EACL.

[28]  Siddharth Patwardhan,et al.  Feature Subsumption for Opinion Analysis , 2006, EMNLP.

[29]  Hiroshi Kanayama,et al.  Fully Automatic Lexicon Expansion for Domain-oriented Sentiment Analysis , 2006, EMNLP.