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SMILE: Twitter Emotion Classification using Domain Adaptation

Bo Wang, Maria Liakata, Arkaitz Zubiaga, Rob Procter, Eric Jensen

Workshop on Sentiment Analysis where AI meets Psychology. 2016.

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Despite the widely spread research interest in social media sentiment analysis, sentiment and emotion classification across different domains and on Twitter data remains a challenging task. Here we set out to find an effective approach for tackling a cross-domain emotion classification task on a set of Twitter data involving social media discourse around arts and cultural experiences, in the context of museums. While most existing work in domain adaptation has focused on feature-based or/and instance-based adaptation methods, in this work we study a model-based adaptive SVM approach as we believe its flexibility and efficiency is more suitable for the task at hand. We conduct a series of experiments and compare our system with a set of baseline methods. Our results not only show a superior performance in terms of accuracy and computational efficiency compared to the baselines, but also shed light on how different ratios of labelled target-domain data used for adaptation can affect classification performance.