A Hybrid Model Integrating Adaboost Approach for Sentimental Analysis of Airline Tweets

Abstract

Sentimental Analysis has grown as a significant opinion strategy in the field of online media due to quick information development and internet technologies. This research will play an important role for recommendation of best airline for Indian passengers to prefer the appropriate airline for their journey and also useful for the Indian ministry of aviation. In this study we have gathered different tiny texts called comments from different social media traveling websites using webharvy data fetcher scraping tool related to six top rated Indian airlines. The main problem with airline tweet SA (sentimental analysis) is determining the best sentiment classifier for appropriately classifying the tweets. VADER model has used sentiment ratings to connect lexical characteristics to emotion intensities. In this research, a Hybrid model integrated Adaboost approach (HMIAA) has proposed, which combines the basic learning classifier SVM with the forward-learning ensemble method Gradient Boosted Tree to form a single robust classifier or model, with the objective of improving SCT (sentimental classification technique) efficiency (performance) and accuracy. The findings reveal that the suggested hybrid approach integrating Adaboost technique outperforms other basic classifiers. After completion of sentimental analysis of all datasets we can recommend the passengers for the best airline.