Airline tweets sentimental analysis using Adaptive rider optimization based support vector neural network

Nowadays, peoples are posting their personal opinion about different brands on social media. Brands such as airlines, cars, sports teams, entertainment, finance, retail, and the food industry are popular on Twitter. Among them, airlines are the most used brand in business people. In airline companies, building customer loyalty is a major challenge. In the past, traveller's reviews are manually read, analyze, and categorize, which required a lot of effort and time. To avoid the problem, an efficient automatic sentimental analysis method is proposed. The main aim of this paper is to fining best and work airline from tweets. The proposed method consists of three stages namely, pre-processing, feature extraction, and sentiment classification. Initially, the collected reviews are pre-processed to reduce the complexity of classification process. After the pre-processing, word2vec conversion is performed using a skip-gram model. Then this output vector is given to the Adaptive Rider optimization based support vector neural network (AR-SVNN) classifier to classify a review as positive, negative, or neutral. Here, the SVNN classifier is enhanced using adaptive rider optimization (ARO) algorithm. The performance of the proposed methodology is analyzed in terms of different metrics namely, accuracy, precision, recall and F-Measure. The experimental results show that proposed method outperformed the other methods.