A Convolutional Neural Network Model for Emotion Detection from Tweets

Abstract

Sentiment analysis and emotion recognition are major indicators of society trends toward certain topics. Analyzing opinions and feelings helps improving the human-computer interaction in several fields ranging from opinion mining to psychological concerns. This paper proposes a deep learning model for emotion detection from short informal sentences. The model consists of three Convolutional Neural Networks (CNNs). Each CNN contains a convolutional layer and a max-pooling layer, followed by a fully-connected layer for classifying the sentences into positive or negative. The model employs the word vector representation as textual features, which works on random initialization for the word vectors, and are set to be trainable and updated through the model training phase. Eventually, task-specific vectors are generated as the model learns to distinguish the meaning of words in the dataset. The model has been tested on the Stanford Twitter Sentiment dataset for classifying sentiment into two classes positive and negative. The presented model achieved to record 80.6% accuracy as a prove that even with randomly initialized word vectors, it can work very well in text classification tasks when trained with CNNs.