Semantic Hashtag Relation Classification Using Co-occurrence Word Information
- 9 Downloads
Users using social networking service (SNS) may express their thoughts and feelings using simple hashtags. Hashtags are related to other hashtags and images that are used together in the user’s other posts. Understanding the meaning of personal hashtags can be a way to learn latent semantic expressions of personal words. Existing methods for learning and analyzing semantics such as Latent Semantic Analysis, Latent Dirichlet Allocation and Word Embedding need large-scale corpus to construct an elaborate model. Large-scale corpus usually consists of words that a lot of people already use. Thus, existing methods are able to catch the latent meaning of words used in general. However, it is difficult for these methods to find personal meanings of words that are used by a particular person. Because the number of words that a person use is usually very small compared to a large-scale corpus. Another reason for the difficulty is that existing methods use occurrence frequency or co-occurrence probability. Therefore, the importance or the frequency or the probability of personalized meaning may disappear because of this large difference in the number of words. In this research we focused on the classification of semantic words using a user’s hashtag data and the co-occurrence of these hashtags. The performance is evaluated and enhances previous work by 18% for Precision and more than 70% for Recall.
KeywordsHashtag Social networking service Semantics Information retrieval Personalized meaning Personal word vector
This work was supported in part by the National Research Foundation of Korea under Grant Number 2014R1A1A2059527.
- 1.Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111–3119).Google Scholar
- 2.Harris, Z. (1954). Distributional structure. (J. Katz, Ed.). Word Journal of the International Linguistic Association, 10(23), 146–162.Google Scholar
- 5.Mikolov, T., Kombrink, S., Burget, L., Černocký, J., & Khudanpur, S. (2011). Extensions of recurrent neural network language models. In IEEE international conference on acoustics, speech and signal processing (ICASSP). Prague, CZ.Google Scholar
- 6.Denton, E., Weston, J., Paluri, M., Bourdev, L., & Fergus, R. (2015). User conditional hashtag prediction for images. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1731–1740). ACM.Google Scholar
- 7.Weston, J., Chopra, S., & Adams, K. (2014). # TagSpace: Semantic embeddings from hashtags. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1822–1827).Google Scholar
- 8.Wang, X., Wei, F., Liu, X., Zhou, M., & Zhang, M. (2011). Topic sentiment analysis in twitter: A graph-based hashtag sentiment classification approach. In Proceedings of the 20th ACM international conference on information and knowledge management (pp. 1031–1040). ACM.Google Scholar
- 9.Bansal, P., Bansal, R., & Varma, V. (2015). Towards deep semantic analysis of hashtags. In European conference on information retrieval. Springer International Publishing, Berlin.Google Scholar
- 10.Seo, S., Kim, J. K., & Choi, L. (2017). Semantic hashtag relation classification using co-occurrence word information. In ICUFN 2017—9th international conference on ubiquitous and future networks (pp. 860–862).Google Scholar