An Intelligent Deep Neural Sentiment Classification Network

Abstract

A Deep Neural Sentiment Classification Network (DNSCN) is developed in this work to classify the Twitter data unambiguously. It attempts to extract the negative and positive sentiments in the Twitter database. The main goal of the system is to find the sentiment behavior of tweets with minimum ambiguity. A well-defined neural network extracts deep features from the tweets automatically. Before extracting features deeper and deeper, the text in each tweet is represented by Bag-of-Words (BoW) and Word Embeddings (WE) models. The effectiveness of DNSCN architecture is analyzed using Twitter-Sanders-Apple2 (TSA2), Twitter-Sanders-Apple3 (TSA3), and Twitter-DataSet (TDS). TSA2 and TDS consist of positive and negative tweets, whereas TSA3 has neutral tweets also. Thus, the proposed DNSCN acts as a binary classifier for TSA2 and TDS databases and a multiclass classifier for TSA3. The performances of DNSCN architecture are evaluated by F1 score, precision, and recall rates using 5-fold and 10-fold cross-validation. Results show that the DNSCN-WE model provides more accuracy than the DNSCN-BoW model for representing the tweets in the feature encoding. The F1 score of the DNSCN-BW based system on the TSA2 database is 0.98 (binary classification) and 0.97 (three-class classification) for the TSA3 database. This system provides better a F1 score of 0.99 for the TDS database.