The Development of Geo-Names Extraction from Twitter Texts Data by Conditional Random Fields
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Abstract
Navigation systems and online maps, Mobile application, and other platforms, are becoming increasingly important due to increasing users and providers. Place names or geonames (geographic names) are essential sources of information that users tend to use as keywords in their searches. Including storing these data in different categories. This research aims to create a model capable of extracting geonames and automatically categorizing them from the social media source of Twitter, one of the popular platforms in Thailand. It is a fast and always up-to-date information source, providing the opportunity to discover new geographic locations and helpful in gathering geospatial information without needing a field survey. Named-entity recognition standard tool cannot be used directly because of the classification of name entities that are not categorized by geographic names. As for the model, the conditional random field algorithm is applied to linguistic features such as place prepositions (near, far, next, next to, etc.) and prefixes, for instance, school, market, temples, villages, etc. This study, the Corpus was created from 28,082 Twitter messages, representing 80 percent of the 22,445 training set and 20 percent of the test set of 5,617 messages. According to the algorithm used to word tokenize, the experiment was designed into two main groups. The study result of the model with the highest overall accuracy (F1) was 0.946, which provided sufficient overall accuracy for relevant applications both on the web browser.
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