SSN_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process Classifier

Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pages 709–712, Vancouver, Canada, August 3 - 4, 2017. c⃝2017 Association for Computational Linguistics SSN MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process Classifier Angel Deborah S, S Milton Rajendram, T T Mirnalinee SSN College of Engineering Kalavakkam 603 110, India angeldeboarahs@ssn.edu.in Abstract The SSN MLRG1 team for Semeval-2017 task 4 has applied Gaussian Process, with bag of words feature vectors and fixed rule multi-kernel learning, for sentiment anal- ysis of tweets. Since tweets on the same topic, made at different times, may exhibit different emotions, their properties such as smoothness and periodicity also vary with time. Our experiments show that, com- pared to single kernel, multiple kernels are effective in learning the simultaneous presence of multiple properties. 1 Introduction Twitter is a huge microblogging service with more than 500 million tweets per day from different lo- cations of the world and in different languages (Nabil et al., 2016). The sentiment analysis in Twitter has been applied in various domains such as commerce (Jansen et al., 2009), disaster man- agement (Verma et al., 2011) and health (Chew and Eysenbach, 2010). The task is challenging because of the informal writing style, the seman- tic diversity of content as well as the “unconven- tional” grammar. These challenges in building a classification model can be handled by using proper approaches to feature generation and ma- chine learning. The heart of every Gaussian process model is a covariance kernel. Multi Kernel Learning (MKL)—using multiple kernels instead of a sin- gle one—can be useful in two ways: • Different kernels correspond to different no- tions of similarity, and instead of trying to find which works best, a learning method does the picking for us, or may use a combi- nation of them. Using a specific kernel may be a source of bias which is avoided by allow- ing the learner to choose from among a set of kernels. • Different kernels may use inputs coming from different representations, possibly from different sources or modalities. (Gonen and Alpaydn, 2011) and (Wilson and Adams, 2013) explain how multiple kernels defi- nitely give a powerful performance. (Gonen and Alpaydn, 2011) also describe in detail various methodologies to combine kernels. (Wilson and Adams, 2013) introduces simple closed form ker- nels that can be used with Gaussian Processes to discover patterns and enable extrapolation. The kernels support a broad class of stationary co- variances, but Gaussian Process inference remains simple and analytic. We studied the possibility of using multiple ker- nels to explain the relation between the input data and the labels. While there is a body of work on using Multi Kernel Learning (MKL) on numerical data and images, yet applying MKL on text is still an exploration. 2 Gaussian Process Gaussian Process is a non-parametric Bayesian modelling in supervised setting. Gaussian pro- cess is a collection of random variables, any fi- nite number of which have a joint Gaussian dis- tribution (Rasmussen and …

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