An Ensemble Multi-Layered Sentiment Analysis Model (EMLSA) for Classifying the Complex Datasets

Abstract

Sentiment analysis is one domain that analyzes the feelings and emotions of the users based on their text messages. Sentiment analysis of short messages, reviews in online social media (OSM), and social networking sites (SNS) messages gives the analysis of given text data. Processing short text and SNS messages is a very tedious task because of the restricted detailed information generally contained. Solving this issue requires advanced techniques that are combined to give accurate results. This paper developed an Ensemble Multi-Layered Sentiment Analysis Model (EMLSA) that exploits the trust-based sentiment analysis on various real-time datasets. EMLA is the combined approach with VADER (Valence Aware Dictionary and sEntiment Reasoned) and Recurrent Neural Networks (RNNs). VADER is the lexicon and rule-based sentiment analysis model that predicts the sentiments extracted from input datasets and it is used for training. The feature extraction technique is term-frequency and inverse document frequency. Word-Level Embeddings (WLE) and Character-Level Embeddings (CLE) are the two models that increase the short text and single-word analysis. The proposed model was applied to four real-time datasets: Amazon, eBay, Trip-advisor, and IMDB Movie Reviews. The performance is analyzed using various parameters such as sensitivity, specificity, precision, accuracy, and F1-score.