Job Detection in Twitter
Table 1: The performance results for the two employed document representations
['[EMPTY]', 'Precision', 'Recall', 'F1-Measure']
For the bag of word model, we represented each document by 5000 terms as features. We used 80 percent of randomly selected labeled dataset for training and its remaining as the test set. We applied a Naive Bayes model over the training set for classification. In the next experiment we used the document representation based on word2vec in our classification. We used word2vec for pretraining and extracting word vectors from a set of timelines of 7237 twitter users. Using the average vectors as described in the previous section we ended up with representing each document by a vector of length 200. Again we used 80 percent of labeled data for training and the remaining for testing. RandomForest model is used for classification. Beside these improvements we may also note that by using pre training we achieved the higher performance by using a highly dense vector representation of only 200 features.