ENHANCEMENTS OF HOT EVENT DETECTION METHOD FOR DETECTING EVENTS BASED ON NEWS DATA
Events are things that actually occur, attract attention, have a location and time, and have consequences such as the emergence of news reporting the event. Therefore, scientists have developed various methods to detect events from news documents in the hope that these events can be further utili...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/53776 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Events are things that actually occur, attract attention, have a location and time, and
have consequences such as the emergence of news reporting the event. Therefore,
scientists have developed various methods to detect events from news documents
in the hope that these events can be further utilized, such as processing location and
time. One such method is called Hot Event Detection, which was developed by Qi
et al. This method was chosen because it is easy to understand, implement and
enhance. However, like other event detection methods, it does not support the
processing of location and time of events. Therefore, this final project adds these
enhancements along with other enhancements such as event merging and keyword
processing. Enhancement for keyword processing is divided into analyzing
keyword extraction and semantic similarity.
In this final project, enhancements to the Hot Event Detection method are
implemented through a combination of various techniques. The location attribute is
obtained accurately through the news text, the time attribute is obtained from the
time of news publication. Event merging is implemented utilizing the DBSCAN
clustering algorithm. The keyword extraction analysis uses the TF-IDF dictionary
to check the relevance of these keywords and WordNet ontology is used for
Semantic Similarity.
Enhancements such as event merging and keyword processing successfully
improve Hot Event Detection performance through better cluster quality. The
enhancement of the addition of the location and time attributes of the event
succeeded in obtaining good results with the correct threshold and attenuation
configuration (71% precision or 79% recall), but deficiencies were still found that
caused the precision or recall to not be higher, for example, clusterization errors.
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