Newspaper’s Editorial Opinion Prediction in Sentiment Analysis Using Deep Learning Methods

Abstract

Sentiment analysis is a computational method that uses preliminary emotion analysis to retrieve feelings and key phrases from various texts (e.g., Positive, Negative, Neutral). It’s necessary to extract useful information from big data, categorize it, and predict end-user behavior or emotions. Text classification is a research area of Natural Language Processing (NLP). Which is converted from unstructured data to meaningful categorical classes. All previous work is most likely based on traditional different classifiers such as KNN, SVM, and so on. In this study, we propose a method which is combined in two familiar deep learning models: Convolutional Neural Networks (CNN) and Bidirectional Long Short Term Memory (BiLSTM). CNN method retrieves greater characteristics by convolutional layers and max pooling layers and BiLSTM can capture long term dependencies by lexical items and it’s better for text classification. Our own built datasets collected from various Bengali newspapers, such as Bangla Tribune, The Daily Sun etc. generate massive amounts of data. Our dataset size is 5996. The accuracy of the proposed model differs optimizer wise. We use three different optimizers: Adam, Adamax, RMSProp. With these three optimizers, the highest outcomes come from Adam optimizer. Accuracy of our first proposed model (BiLSTM CNN) with word2vec and Glove word embedding is 91.59% and 99.40%. In (CNN-BiLSTM) methods obtained outcomes are 92.60% and 94% and the final model (BiLSTM-CNN-BiLSTM) got their results 82.79% and 98.47%. The experimental results represent how the deep learning models effectively work. We say that the amount and quality of training examples have a significant impact on models performance.