The ongoing debate and politicization of climate change, despite an overwhelming scientific consensus, underscore the need for a comprehensive analysis of public sentiment. Our study investigates one particular dataset of over 600M Twitter posts in English to understand how the climate change sentiment has changed over time. Specifically, we seek to analyse how attitudes have changed and investigate the contextual usage of words related to climate change. To achieve this, we trained a series of Word2vec models, one for each month, to scrutinize temporal shifts in language usage. We also fine-tune a BERT model to perform sentiment classification on tweets, enhancing our understanding of prevailing sentiments.