Abstract
Twitter, a Social networking service produce a huge quantity of data daily for many trending real-world events. As hundreds of millions of Twitter users generate many posts on a daily basis, therefore it’s very challenging to extract and summarize the user-generated content. Moreover, the Twitter API also provides only latest posts in a sequential order. This motivates the dire need for a new automatic event summarization system that provides the informative summaries of user-generated content that might help in making decisions supporting intelligence. In this paper, we intend to summarize the twitter posts corresponding to twitter hashtags to find a representative post among a set of posts that correspond to the same hashtag, with the intent to identify the strongly relevant post. We used two approaches Temporal TF-IDF and Temporal TF-IDF with keyword importance for finding the summary of the events. Then we evaluate and compare these approaches using a self-generated dataset of Twitter posts and show that our system automatically select posts that are more relevant.
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Maryam, A., Ali, R. (2018). Twitter Based Event Summarization. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 905. Springer, Singapore. https://doi.org/10.1007/978-981-13-1810-8_17
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DOI: https://doi.org/10.1007/978-981-13-1810-8_17
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