Benchmarking Machine Learning for Sentimental Analysis of Climate Change Tweets in Social Internet of Things

Climate change has become one of the most critical problems threatening our world, gaining an increased attention in either academia or industry. Climate change has been demonstrated as the major barrier in the way of sustainable development strategy in 2030 Agenda. Nowadays, Social Internet of Things (SIoT) have paved new ways for public deliberations and have transformed the communication of global issues such as climate change. Thus, sentiment analysis of SIoT media streams can offer a great help for improving the mitigation and adaption to climate changes. Machine learning (ML) is a demonstrating great success in wide range of SIoT applications. However, training ML algorithms for sentimental analysis of climate change is notoriously hard as it suffers from feature engineering issues, information squashing, unbalancing, curse-of-dimensionality, which bounds their possible power for modeling social awareness of climate change. Besides, the absence of standard benchmark with reasonable and dependable experimentations brings a practically intractable difficulty to the evaluation of the efficiency of new solutions. In this regard, this study introduces the first reasonable and reproducible benchmark devoted to evaluating the potentials of ML algorithms in identifying the users’ opinions about climate change. Moreover, a novel taxonomy is presented for categorizing the existing ML algorithms, exploring their optimal hyperparameter, and unifying their elementary settings. Inclusive experiments are then performed on real twitter data with different families of ML algorithms. To promote further study, a detailed analysis is provided for the state of the field to uncover the open research challenges and promising future directions.