Emotions from Farsi texts with mutual-word-counting and word-spotting

Automatic emotion sensing of textual data plays a crucial role in development of many intelligent applications. The objective of this paper is to design a system for sensing of a Farsi sentence emotion. The final goal of this work is to use it with Farsi TTS and SDS systems and make it possible to synthesis sentences with their emotions. Four emotions where considered and four sensing methods including (1) word counting, (2) mutual word counting, (3) word-spotting, and (4) combination of mutual word counting and word-spotting were used. The performance of this system was evaluated using these methods including 10-Fold Cross-Validation and was shown that the combination of mutual word counting and word-spotting is the best with the accuracy of about 82%. It was observed that the amount of labeled training data and the type of sentences have an important role in the system performance.