Abstract
Social media such as Twitter, has become a valuable source for mining opinions of users about all kinds of topics. In this paper, we investigate how to automatically integrate topic related opinions expressed by a user in User-Generated Content (UGC). We propose a general subjectivity model by combining topics and fine-grained opinions towards each topic, and design an efficient algorithm to establish the model. We demonstrate utility of our model in the opinion prediction problem and verify the effectiveness of our model qualitatively and quantitatively in a series of experiments on real Twitter data. Results show that the proposed model is effective and can generate consistent integrated opinion summaries for users. Furthermore, the proposed model is more suitable for social media context, thus can reach better performance in an opinion prediction task.
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Xie, S., Tang, J., Wang, T. (2014). Topic Related Opinion Integration for Users of Social Media. In: Huang, H., Liu, T., Zhang, HP., Tang, J. (eds) Social Media Processing. SMP 2014. Communications in Computer and Information Science, vol 489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45558-6_15
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DOI: https://doi.org/10.1007/978-3-662-45558-6_15
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