Unsupervised and Interpretable Domain Adaptation to Rapidly Filter Social Web Data for Emergency Services
Table 7. Comparison of four relevant word embedding models on TREC Sentiment task.
['TARGET', 'fastText (Mikolov et al.,  2018 ) Acc', 'fastText (Mikolov et al.,  2018 ) F1', 'GoogleNews (Mikolov et al.,  2013 ) Acc', 'GoogleNews (Mikolov et al.,  2013 ) F1', 'Glove (Pennington et al.,  2014 ) Acc', 'Glove (Pennington et al.,  2014 ) F1', 'CrisisNLP (Imran et al.,  23-28 ) Acc', 'CrisisNLP (Imran et al.,  23-28 ) F1']
We conducted experiments to classify TREC Sentiment tweets using four choices of word embeddings: fastText All of them performed similarly with Glove performing slightly better than the rest. Glove vectors are 200-dimensional while the rest are 300-dimensional which also makes the experiment favoring Glove word vectors. This experiment shows that the problem of finding a good word vector model for tweets still remains a challenging task.