National happiness index monitoring using Twitter for bilanguages

Abstract

Nowadays, social media have become one of the most important methods of communication that provide a real-time and rich source of information, including sentiments. Understanding the population sentiment is a key goal for organisations and governments. In recent years, quite a lot of research has been done on sentiment analysis from social media. However, all the work in the state of the art is focused on a specific pre-defined subset of tweets, e.g. sentiment analysis via keywords search from tweets for relevant brands, products, services, events and so forth. Monitoring the general sentiment at national level through the whole social media stream is not done due to the challenges of filtering sentiment-irrelevant information, diversity of vocabulary usage in general tweets across topics causing low accuracy and the need for bilingual or multilingual models. This paper proposes a system for general population sentiment monitoring from a social media stream (Twitter), through comprehensive multi-level filters, and our proposed improved latent Dirichlet allocation (LDA) (Wang et al. in ACM Trans Internet Technol 18(1):1–23, 2017; Wang and Al-Rubaie in Appl Soft Comput 33:250–262, 2015; https://patents.google.com/patent/US20170293597A1/en) method for sentiment classification. Experiments show that our proposed improved LDA for sentiment analysis yields the best results, and also validate our proposed system for national sentiment monitoring in Abu Dhabi using twitter.