Exploiting Opinion Influence in Question Answering Systems

This paper proposes a question-answering approach capable of influencing human opinions. We rely on the assumption that humans are inclined to react positively if confronted with a positive situation. Our work is based on up to date opinion mining techniques which we link to the answer candidate generation to produce the question-answering system. We propose a new process of candidate answer selection with regard to both similarity and opinion by introducing a trade-off parameter which also can be adapted on-line to allow for situation specific answers. The presented approach is intended to serve as a basis for a class of future investigations and developments.

[1]  John Blitzer,et al.  Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification , 2007, ACL.

[2]  Ewan Klein,et al.  Natural Language Processing with Python , 2009 .

[3]  Patricio Martínez-Barco,et al.  Opinion and Generic Question Answering Systems: a Performance Analysis , 2009, ACL/IJCNLP.

[4]  Jimeng Sun,et al.  Social influence analysis in large-scale networks , 2009, KDD.

[5]  Jiawei Han,et al.  Mining topic-level influence in heterogeneous networks , 2010, CIKM.

[6]  Petr Sojka,et al.  Software Framework for Topic Modelling with Large Corpora , 2010 .

[7]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[8]  Kwong-Sak Leung,et al.  A Survey of Crowdsourcing Systems , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[9]  Jure Leskovec,et al.  Discovering value from community activity on focused question answering sites: a case study of stack overflow , 2012, KDD.

[10]  Yong Shi,et al.  The Role of Text Pre-processing in Sentiment Analysis , 2013, ITQM.

[11]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[12]  Diana Inkpen,et al.  uOttawa: System description for SemEval 2013 Task 2 Sentiment Analysis in Twitter , 2013, *SEMEVAL.

[13]  Jimeng Sun,et al.  Confluence: conformity influence in large social networks , 2013, KDD.

[14]  Cícero Nogueira dos Santos,et al.  Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts , 2014, COLING.

[15]  Quoc V. Le,et al.  Distributed Representations of Sentences and Documents , 2014, ICML.

[16]  Houshmand Shirani-mehr,et al.  Applications of Deep Learning to Sentiment Analysis of Movie Reviews , 2015 .

[17]  Hadi Pouransari,et al.  Deep learning for sentiment analysis of movie reviews , 2015 .

[18]  Wang Mengting,et al.  Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems , 2016 .

[19]  Julian J. McAuley,et al.  Addressing Complex and Subjective Product-Related Queries with Customer Reviews , 2015, WWW.

[20]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[21]  Amit Mishra,et al.  A survey on question answering systems with classification , 2016, J. King Saud Univ. Comput. Inf. Sci..

[22]  Preslav Nakov,et al.  SemEval-2016 Task 4: Sentiment Analysis in Twitter , 2016, *SEMEVAL.

[23]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[24]  Ioannis Hatzilygeroudis,et al.  A System for Aspect-based Opinion Mining of Hotel Reviews , 2017, WEBIST.