UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination

We present a novel method for determining sentiment intensity. The main goal is to assign a phrase a score from 0 to 1 which indicates the strength of its association with positive sentiment. The proposed model uses a rich set of features with Gaussian processes regression model that computes the final score. The system was evaluated on the data from 7th task of SemEval 2016. Our regression model trained on the development data reached Kendall rank correlation of 0.659 on general English phrases and 0.414 on English Twitter test data.

[1]  Christopher Potts,et al.  Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.

[2]  Andrea Esuli,et al.  SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining , 2010, LREC.

[3]  Preslav Nakov,et al.  SemEval-2015 Task 10: Sentiment Analysis in Twitter , 2015, *SEMEVAL.

[4]  Soo-Min Kim,et al.  Determining the Sentiment of Opinions , 2004, COLING.

[5]  Andrea Esuli,et al.  SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining , 2006, LREC.

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

[7]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[8]  Ramón Fernández Astudillo,et al.  INESC-ID: Sentiment Analysis without Hand-Coded Features or Linguistic Resources using Embedding Subspaces , 2015, *SEMEVAL.

[9]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[10]  Josef Steinberger,et al.  Creating Sentiment Dictionaries via Triangulation , 2011, Decis. Support Syst..

[11]  Saif Mohammad,et al.  SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases , 2016, *SEMEVAL.

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

[13]  Xiaolong Wang,et al.  Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach , 2011, CIKM '11.

[14]  Xiaoyan Zhu,et al.  Sentiment Analysis with Global Topics and Local Dependency , 2010, AAAI.

[15]  Zhihua Zhang,et al.  ECNU: Multi-level Sentiment Analysis on Twitter Using Traditional Linguistic Features and Word Embedding Features , 2015, *SEMEVAL.