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.

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.

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