Enhancing Sentiment Analysis in the Airline Sector: A Deep Learning Approach Using Twitter Data
Abstract
The airline sector has been increasingly entangled with Twitter data owing to its real-time nature and vast usage; therefore, airlines are trying to gain a better understanding of customer perspectives and trying to solve them as quickly as possible. Thus, opinion mining has emerged as one of the vital tools of survival for airlines in competitive scenarios by understanding their customer opinions and improving their business strategies accordingly. Twitter is a valid data source because of its real-time traits and immense number of users. In this work, the tweets were changed to numerical vectors by the Word2Vec word embedding methods in a deep neural network. Using TF-IDF and a few traditional machine learning (ML) frameworks for sentiment categorization, the productivity of this model is contrasted with that of a deep neural network. It does an evaluation, too, on the performance of frameworks in comparison to a neural network architecture: XGBClassifier, LGBMClassifier, ExtraTreeClassifier, AdaBoostClassifier, BernoulliNB, and NearestCentroid. The outcomes of the investigation displayed that the recommended framework, which incorporates neural networks and word embedding techniques, achieved a remarkable accuracy of 0.88 in sentiment classification tasks on Twitter data.References
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