Improving the Performance of Sentiment Analysis Using Enhanced Preprocessing Technique and Artificial Neural Network

• Published in: IEEE transactions on affective computing 2022-10, Vol.13 (4), p.1771-1782
• Main Authors: Thakkar, Ankit, Mungra, Dhara, Agrawal, Anjali, Chaudhari, Kinjal
• Format: Article
• Language: English
• Subjects: Artificial neural network; Artificial neural networks; bernoulli naïve bayes; convolutional neural network; Data mining; Deep learning; dranziera; Feature extraction; linear support vector classifier; logistic regression; Motion pictures; Neural networks; Neurons; neurosent; order of preprocessing; Performance evaluation; Preprocessing; Sentiment analysis; Social networking (online); Support vector machines; Task analysis; weight initialization
• ISSN: 1949-3045; 1949-3045
• DOI: 10.1109/TAFFC.2022.3206891

With the presence of a massive amount of digitally recorded data, an automated computation can be preferable over the manual approach to evaluate sentiments within given textual fragments. Artificial neural network (ANN) is preferred for sentiment analysis (SA) because of its learning ability and adaptive nature towards diverse data. Handling negation in SA is a challenging task, and to address the same, we propose a specific order of preprocessing (PPR) steps to enhance the performance of SA using ANN. Typically, ANN weights are randomly initialized (R-ANN), which may not give the desired performance. As a potential solution, we propose a novel approach named Matching features with output label based Advanced Technique (MAT) to initialize the ANN weights (MAT-ANN). Simulation results conclude the superiority of the proposed approach PPR+MAT-ANN compared to the existing approach EPR+R-ANN i.e., integrating existing preprocessing (EPR) steps with R-ANN. Moreover, PPR+MAT-ANN architecture is significantly simpler than the existing deep learning-based approach named the NeuroSent tool and gives better performance when evaluated upon the Dranziera protocol.