Implicit Dimension Identification in User-Generated Text
Table 3: Political perspective detection: Baseline classification results.
['[BOLD] Model', '[BOLD] Classification subject', '[BOLD] Class-1 (Israeli) Recall', '[BOLD] Class-2 (Palestinian) Recall', '[BOLD] AUC']
Baseline We experimented with different features representing the articles during the baseline experiments. As in the case of CQA task we tried unigram, bi-gram and tri-gram features. We also identified that tf-idf features used as signal representation achieved a higher result. We used the following baseline algorithms and configurations: SVM with tf-idf features, using sigmoid kernel and penalty variable C=1. Multinomial Naive Bayes classifier on tf-idf features with the alpha variable C=1. Random Forest on tf-idf features with 10 estimators, splitting on the Gini criterion. Decision Tree Classifier on tf-idf features. No limitation on max depth, splitting on the Gini criterion. Logistic Regression on tf-idf features using l2 penalty, with the inverse of regularization at 1 and maximum number of iterations 100.