  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.
