Text emotion detection in social networks using a novel ensemble classifier based on Parzen Tree Estimator (TPE)

The texts often express the emotions of the writers or cause emotions in the readers. In recent years, the development of the social networks has made emotional analysis of texts into an attractive topic for research. A sentiment analysis system for automatic detection of fine-grained emotions in text consists of three main parts of preprocessing, feature extraction and classification. The main focus of this paper is on presenting a novel ensemble classifier that is consisted of 1500 of k-Nearest Neighbor, Multilayer Perceptron and Decision Tree basic classifiers, which is able to systematically distinguish different fine-grained emotions between regular and irregular sentences with a proper accuracy. Moreover, Tree-structured Parzen Estimator is employed to tune parameters of the basic classifiers. The preprocessing and feature extraction operations are performed by natural language processing tools (Tokenization and Lemmatization) and Doc2Vector algorithm, respectively. Three different sets of ISEAR, OANC and CrowdFlower are used to evaluate the proposed method, which consists of regular and irregular sentences. The evaluation results show that accuracies of the proposed ensemble classifier are 99.49 and 88.49% in the detection of regular and irregular sentences, respectively.