YNUDLG at SemEval-2017 Task 4: A GRU-SVM Model for Sentiment Classification and Quantification in Twitter

Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pages 713–717, Vancouver, Canada, August 3 - 4, 2017. c⃝2017 Association for Computational Linguistics YNUDLG at SemEval-2017 Task 4: A GRU-SVM Model for Sentiment Classification and Quantification in Twitter Min Wang1, Biao Chu1, Qingxun Liu1, Xiaobing Zhou1 1School of Information Science and Engineering, Yunnan University, Yunnan, P.R. China {1147192499, 372744991, 1243305076, zhouxb.yn}@qq.com Abstract Sentiment analysis is one of the central issues in Natural Language Processing and has bec- ome more and more important in many fields. Typical sentiment analysis classifies the sent- iment of sentences into several discrete class- es (e.g.,positive or negative). In this paper we describe our deep learning system(combining GRU and SVM) to solve both two-, three- and five- tweet polarity classifications. We first trained a gated recurrent neural network using pre-trained word embeddings, then we extra- cted features from GRU layer and input these features into support vector machine to fulfill both the classification and quantification subtasks. The proposed approach achieved 37th, 19th, and 14rd places in subtasks A, B, and C, respectively. 1 Introduction Sentiment analysis (SA) is a field of knowledge which deals with the analysis of people’s opinions, sentiments, evaluations, appraisals, attitudes and emotions towards particular entities (Liu, 2012). Typical approaches to sentiment analysis is to classify the sentiment of a sentence into several discrete classes such as positive and negative polarities, or six basic emotions: anger, happiness, fear, sadness, disgust and surprise (Ekman,1992). SA is widely considered to be one of the most popular and challenging, competitive and the hot research area in computational linguistics. There are many ways to tackle the sentiment classification problems, such as random forest, support vector machine (SVM), Bayes classifier. In addition, there are many cha- llenges, such as analysis of noise texts (e.g. oral language) in natural language processing tasks, despite numerous notable advances in recently years (e.g., Breck et al,. 2007; Yessenalina and Cardie, 2011; Socher et al,. 2011). Based on this, our way is to extract features with Gated Recurrent Unit (GRU) and classify sentences by SVM using these features. Task 4 subtask A is to classify a tweet’s sentiment as positive, negative, or neutral. Subtask B (Tweet classification according to a two-point scale) requires classifying a tweet’s sentiment towards the given topic. Similar to B, subtask C is a five-point scale (Nakov et al., 2016). Unlike typical classification approaches, ordinal classification can assign different ratings (e.g., very negative, negative, neutral, positive and very positive) according to the sentiment strength (Taboada et al., 2011; Li et al., 2011; Yu et al., 2013; Wang and Ester, 2014). This paper presents a system that combine GRU and SVM to process subtasks A, B and C. Our system uses a GRU neural network with word embeddings (Mikolov et al,. 2013) that are slightly fine-tuned (Yoon Kim et al,. 2014) on each training set. The word embeddings were obtained by training GloVe (Jeffrey Pennington et al,. 2014) on 2 billion tweets that we crawled for this purpose. These …

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