No abstract

Emotions are one of the most important and affective start points of our decisions in daily life. People express their emotions, consciously or unconsciously, in their behaviors, like speech, facial expressions, textual content, and so on, whether it be in blogs, reviews, or on social media. Due to the rapid growth in textual social media to link people, the emotion extraction from textual data has attracted a lot of attention. This paper performs the sentence-level discrete emotion extraction from text by using Fuzzy Neural Networks (FNN) and Deep Recurrent Neural Networks (DRNN). Much work has been presented to extracting emotion from textual data with precise and constant boundaries, but little of it has considered the inherent uncertainty in natural language. In other words, the high ambiguity of emotion in text data makes an emotionally sentence express multiple emotions at the same time. To deal with this uncertainty we use a Neuro Neural Network (NNN). But from another point of view, by increasing the number of inputs, the number of parameters in NNN growth exponentially. To this end, we extract a low-dimensional semantic representation of input sentence by using a Bi-directional Long Short-Term Memory (Bi-LSTM). Our goal is to achieve better performance with combining the advantages of deep learning in high-level semantic feature extraction to ambiguity handling and the capabilities of fuzzy logic with fuzzy membership degrees to uncertainty handling. Experiments are conducted on the SemEval2007-Task14 dataset that contains 1250 annotated texts with Ekman's basic emotions. The obtained results indicate that our proposed method outperforms the previous methods.