Text-Based Airline Sentiments Analysis Using Deep Learning Ensemble Optimization

Abstract

Text Sentiment Analysis is one of the foundational pillars of sentiment analysis. There hasn't been any research done yet that makes the use of the combined power of modern models and contextual information present in texts, nor has it been studied how contextual information may be controlled in any effective manner. It has been demonstrated that the model proposed in this paper can be used to accurately and efficiently classify sentiments for a given topic. The system is able to capture all of the content contained within the text by combining rule-based methods and deep learning models. In a study undertaken by our group, it has been found that building an embedded representation and attention mechanism is effective in the processing of valence shifting cases. Our proposed mechanism also combines custom-designed rules along with domain-specific sentiment dictionaries. The proposed method has been tested using three datasets and has shown superior performance to other methods, regardless of its higher cost as compared to other methods. PPD (Probability Proportion Difference) is used in this study to pick characteristics that regard each term to be part of a class list. It's a quick and easy way to remove irrelevant words from a feature vector. Categorical Probability Proportion Difference (CPPD) and Probability Proportion Difference (PPD) are used to choose features in a suggested method (CPD). In order to identify features that effectively distinguish between classes and deliver the necessary outcomes, CPPD features selection methods can be used the comparison of the proposed methods' performance with that of the CPD method and the Information Gain (IG) approach shows that all three methods are comparable in their ability to detect sentiment. Two standard datasets (each containing a number of movie reviews and a number of book reviews) have been used as data sources for testing suggested feature selection methods. A study conducted by the authors of this article suggests that the CPPD feature selection method proposed in this article performs better for sentiment classification than other methods. The proposed model is fast and uses the Virgin America Airlines reviews dataset for training and testing. The Monkey learn model achieved 75% precision, 91% recall, and 89% of accuracy, showing the model capabilities in real time Text sentiment analysis.