Abstract
Polarity analysis of Social Media content is of significant importance for various applications. Most current approaches treat this task as a classification problem, demanding a labeled corpus for training purposes. However, if the learned model is applied on a different domain, the performance drops significantly and, given that it is impractical to have labeled corpora for every domain, this becomes a challenging task. In the current work, we address this problem, by proposing an ensemble classifier that is trained on a general domain and and adapts, without the need for additional ground truth, on the desired (test) domain before classifying a document. Our experiments are performed on three different datasets and the obtained results are compared with various baselines and state-of-the-art methods; we demonstrate that our model is outperforming all out-of-domain trained baseline algorithms, and that it is even comparable with different in-domain classifiers.
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Tsakalidis, A., Papadopoulos, S., Kompatsiaris, I. (2014). An Ensemble Model for Cross-Domain Polarity Classification on Twitter. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2014. WISE 2014. Lecture Notes in Computer Science, vol 8787. Springer, Cham. https://doi.org/10.1007/978-3-319-11746-1_12
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DOI: https://doi.org/10.1007/978-3-319-11746-1_12
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