By Mohamed Medhat Gaber, Mihaela Cocea, Nirmalie Wiratunga, Ayse Goker
This quantity offers a suite of rigorously chosen contributions within the quarter of social media research.
Each bankruptcy opens up a couple of examine instructions that experience the capability to be taken on additional during this quickly growing to be sector of study.
The chapters are diversified adequate to serve a few instructions of analysis with Sentiment research because the dominant subject within the book.
The authors have supplied a vast diversity of study achievements from multimodal sentiment id to emotion detection in a chinese language microblogging website.
The publication may be priceless to investigate scholars, teachers and practitioners within the region of social media research.
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Extra resources for Advances in Social Media Analysis
Martin et al. as the fraction of messages in the same slot that contain both of them, so it is highly likely that the term clusters whose similarities are high represent the same topic. The clustering is repeated until the similarity between the nearest un-merged clusters falls below a fixed threshold θ , producing the final set of topic clusters for a set of tweets. 5 which means that two terms must appear in at least half of the same tweets in order to belong to the same topic. Note that this threshold implicitly defines the number of clusters that the system returns for any given set of tweets.
Having found bursts of potentially newsworthy n-grams, we then group together n-grams that tend to appear in the same tweets. Each of these clusters defines a topic as a list of n-grams. 2 Topic Clustering An isolated word or phrase is often not very informative, but a group of them can define the essence of a story. Therefore, we group the most representative n-grams into clusters, each representing a single topic. A group of messages that discuss the same topic will tend to contain at least some of the same n-grams.
31]. Overall, our submission was placed second out of the eleven teams from round the world that completed the challenge. The winning team of Ifrim et al. also used our BNgram approach to rank and filter topics, alongside more aggressive pre-processing and filtering methods . While neither team found every one of the target topics defined by the challenge organizers, the fact that the two best-placed teams used variations of the same BNgram algorithm strongly suggests that this is a robust and flexible tool for detecting topics in Twitter streams.