Abstract
User occupation plays an important role in many applications such as personalized recommendation and targeted advertising. However, user occupations on microblogging platforms are usually unavailable to public due to personal privacy. This opens an interesting problem, i.e., how to predict user occupations on microblogging platforms. In this paper, we propose a framework for extracting user occupations on microblogs. In particular, we implement a number of classification models and devise various sets of features for predicting user occupations, and devise an occupation-oriented lexicon to generate the training data. The experimental results show that the proposed lexicon-based method can achieve higher accuracy compared with traditional models.
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Acknowledgements
This work is supported by the National Science Foundation of China (61379037 and 71273010).
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Lv, X., Jin, P., Yue, L. (2016). User Occupation Prediction on Microblogs. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9932. Springer, Cham. https://doi.org/10.1007/978-3-319-45817-5_54
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DOI: https://doi.org/10.1007/978-3-319-45817-5_54
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