A New Model for Emotions Analysis in Social Network Text Using Ensemble Learning and Deep learning

Abstract

Recently, emotion analysis has become widely used. Therefore, increasing the accuracy of existing methods has become a challenge for researchers. The proposed method in this paper is a hybrid model to improve the accuracy of emotion analysis; Which uses a combination of convolutional neural network and ensemble learning. In the proposed method, after receiving the dataset, the data is pre-processed and converted into process able samples. Then the new dataset is split into two categories of training and test. The proposed model is a structure for machine learning in the form of ensemble learning. It contains blocks consisting of a combination of convolutional networks and basic classification algorithms. In each convolutional network, the base classification algorithms replace the fully connected layer. Evaluate the proposed method, in IMDB, PL04 and SemEval dataset with accuracy, precision, recall and F1 criteria, shows that, on average, for all three datasets, the precision of polarity detection is 90%, the recall of polarity detection is 93%, the F1 of polarity detection is 91% and finally the accuracy of polarity detection is 92%.

Sentiment Analysis Based on Hybrid Neural Network Techniques Using Binary Coordinate Ascent Algorithm

“…No of how useful a given "word vector" may or may not be, each collection of numbers is considered to have been included. In this collection, we've gathered word vectors that our apps can use to understand better words and the relationships between them, as well as the content of individual words [23].…”