Abstract
Twitter has become an important source for people to collect opinions to make decisions. However the amount and the variety of opinions constitute the major challenge to using them effectively. Here we consider the problem of finding propagated opinions – tweets that express an opinion about some topics, but will be retweeted. Within a learning-to-rank framework, we explore a wide of spectrum features, such as retweetability, opinionatedness and textual quality of a tweet. The experimental results show the effectiveness of our features for this task. Moreover the best ranking model with all features can outperform a BM25 baseline and state-of-the-art for Twitter opinion retrieval approach. Finally, we show that our approach equals human performance on this task.
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Luo, Z., Tang, J., Wang, T. (2013). Propagated Opinion Retrieval in Twitter. In: Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G. (eds) Web Information Systems Engineering – WISE 2013. WISE 2013. Lecture Notes in Computer Science, vol 8181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41154-0_2
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DOI: https://doi.org/10.1007/978-3-642-41154-0_2
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