A Novel Deep Learning based Sentiment Analysis of Twitter Data for US Airline Service

The airline industry has evolved quite dynamically over the last two decades. Airline firms use traditional customer feedback types that are very routine and time-intensive. Sentiment analysis may be a crucial approach to the analysis of input in order to minimize the problem. Twitter data acts as a valuable method for gathering user tweets and viewpoint analyzes. This paper proposed a novel deep learning model that effectively combines different word embedding with deep learning methods to evaluate a dataset made up of tweets for six major US Airlines and multi-class sentiment analysis. System selections integrate these features with different deep-learning approaches for term embedding and classify sentimental documents. This methodology starts with raw DNN data extraction and tweet-cleaning pre-processing methods for CNN. The test set product is a positive/negative/neutral tweet interpretation with a 3-class data set and data set precision assessment. Finally, we understand the findings obtained from the models presented by various researchers and prove that our model is more reliable than the previous frameworks.