A Speech Act Classifier for Persian Texts and its Application in Identifying Rumors
This work addresses the need for better natural language processing tools in Persian, specifically for analyzing social media content like rumors, though it is incremental as it adapts existing methods to a new language and application.
The study tackled the problem of Speech Act (SA) classification for Persian texts by developing a dictionary-based statistical technique that achieved state-of-the-art performance with 0.95 accuracy using Random Forest and SVM classifiers. It applied this method to identify common SAs in rumors, finding that Persian rumors often involve narrative, question, and threat classes.
Speech Acts (SAs) are one of the important areas of pragmatics, which give us a better understanding of the state of mind of the people and convey an intended language function. Knowledge of the SA of a text can be helpful in analyzing that text in natural language processing applications. This study presents a dictionary-based statistical technique for Persian SA recognition. The proposed technique classifies a text into seven classes of SA based on four criteria: lexical, syntactic, semantic, and surface features. WordNet as the tool for extracting synonym and enriching features dictionary is utilized. To evaluate the proposed technique, we utilized four classification methods including Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbors (KNN). The experimental results demonstrate that the proposed method using RF and SVM as the best classifiers achieved a state-of-the-art performance with an accuracy of 0.95 for classification of Persian SAs. Our original vision of this work is introducing an application of SA recognition on social media content, especially the common SA in rumors. Therefore, the proposed system utilized to determine the common SAs in rumors. The results showed that Persian rumors are often expressed in three SA classes including narrative, question, and threat, and in some cases with the request SA.