Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment Classification

Proceedings of the 12th International Workshop on Semantic Evaluation (SemEval-2018), pages 218–225 New Orleans, Louisiana, June 5–6, 2018. ©2018 Association for Computational Linguistics Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment Classification Alon Rozental∗, Daniel Fleischer∗ Amobee, Tel Aviv, Israel alon.rozental@amobee.com daniel.fleischer@amobee.com Abstract This paper describes the participation of Amobee in the shared sentiment analysis task at SemEval 2018. We participated in all the English sub-tasks and the Spanish va- lence tasks. Our system consists of three parts: training task-specific word embeddings, training a model consisting of gated-recurrent- units (GRU) with a convolution neural net- work (CNN) attention mechanism and training stacking-based ensembles for each of the sub- tasks. Our algorithm reached 3rd and 1st places in the valence ordinal classification sub- tasks in English and Spanish, respectively. 1 Introduction Sentiment analysis is a collection of methods and algorithms used to infer and measure affection expressed by a writer. The main motivation is enabling computers to better understand human language, particularly sentiment carried by the speaker. Among the popular sources of textual data for NLP is Twitter, a social network service where users communicate by posting short mes- sages, no longer than 280 characters long—called tweets. Tweets can carry sentimental information when talking about events, public figures, brands or products. Unique linguistic features, such as the use of slang, emojis, misspelling and sarcasm, make Twitter a challenging source for NLP re- search, attracting the interest of both academia and the industry. Semeval is a yearly event in which international teams of researchers work on tasks in a com- petition format where they tackle open research questions in the field of semantic analysis. We par- ticipated in Semeval 2018 task 1, which focuses on sentiment and emotions evaluation in tweets. There were three main problems: identifying the ∗These authors contributed equally to this work. presence of a given emotion in a tweet (sub-tasks EI-reg, EI-oc), identifying the general sentiment (valence) in a tweet (sub-tasks V-reg, V-oc) and identifying which emotions are expressed in a tweet (sub-task E-c). For a complete description of Semeval 2018 task 1, see the official task description (Mohammad et al., 2018). We developed an architecture based on gated- recurrent-units (GRU, Cho et al. (2014)). We used a bi-directional GRU layer, together with a convolutional neural network (CNN) attention- mechanism, where its input is the hidden states of the GRU layer; lastly there were two fully connected layers. We will refer to this architecture as the Amobee sentiment classifier (ASC). We used ASC to train word embeddings to incorporate sentiment information and to classify sentiment using annotated tweets. We participated in all the English sub-tasks and in the valence Spanish sub- tasks, achieving competitive results. The paper is organized as follows: section 2 describes our data sources, section 3 describes the data pre-processing pipeline. A description of the main architecture is in section 4. Section 5 de- scribes the word embeddings generation; section 6 describes the extraction of features. In section …

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