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Twitter is a well-known microblogging social site where users express theirviews and opinions in real-time. As a result, tweets tend to contain valuableinformation. With the advancements of deep learning in the domain of naturallanguage processing, extracting meaningful information from tweets has become agrowing interest among natural language researchers. Applying existing languagerepresentation models to extract information from Twitter does not oftenproduce good results. Moreover, there is no existing language representationmodels for text analysis specific to the social media domain. Hence, in thisarticle, we introduce two TweetBERT models, which are domain specific languagepresentation models, pre-trained on millions of tweets. We show that theTweetBERT models significantly outperform the traditional BERT models inTwitter text mining tasks by more than 7% on each Twitter dataset. We alsoprovide an extensive analysis by evaluating seven BERT models on 31 differentdatasets. Our results validate our hypothesis that continuously traininglanguage models on twitter corpus help performance with Twitter.

TweetBERT: A Pretrained Language Representation Model for Twitter Text Analysis