Key Word Extraction Sample Clauses

Key Word Extraction. Besides generating the timestamps of possible social occurrences, another principal component of event detection is to collect a group of words to describe the reporting events. The common approaches to achieve word assembly are Topic Modeling and Named-entity recognition [19]. Section
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Key Word Extraction. 5.3.1 Topic Modeling with Gensim on Tweets After storing each tweet as an individual file, topic modeling is performed to generate word clustering. We choose to extract five topics every time we employ Topic Modeling, the reason is two-fold. Firstly, we manually go over covid-related reports on news websites and determine that on a day where Figure 5.6: Topic Modeling of New-York-related Tweets on March 7th there are some emergent news, the numbers of such news never exceeds 5. Secondly, we run Gensim multiple times and choose different number of topics. After comparing the results, we discover that setting the number of topics to be between 5 and 10 yield the most ideal result (graphs containing segregated and relatively large bubbles). As shown in Figure 5.6 and Figure 5.7, the bubbles generated by Gensim are in reasonable size and scattered in different quadrants, indicating that there are common subjects among the documents. However, the extracted words are mostly verbs, giving little information about other key components of an event, such as who, why, and how. Thus, we still need more words to know the details of the events, a task that will be fulfilled by NER in the Section 5.3.3 and 5.3.4. Figure 5.7: Topic Modeling of New-York-related Tweets on March 9th

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