Exploring Natural Language Processing Techniques in Social Media Analysis during a Pandemic: Understanding a corpus of Facebook posts using Word2vec and LDA

People around the world have used social media extensively to communicate and express opinions especially during this time of the rapid spread of COVID-19. Nowadays, the various narratives of social media users are important that can be used in creating measures to curb the deadly disease. However, the manual collection of data from social media such as Facebook and its analysis can take time. Thus, this study attempted to use natural language processing (NLP) techniques such as topic modeling and word embedding to identify the concepts contained in the posts and comments of Facebook users in the Philippines regarding the pandemic. This study harvested posts and comments in Facebook groups that are primarily Filipino citizens that express opinions and suggestions in COVID-19 responses. Using Latent Dirichlet Allocation (LDA), this study was able to generate 10 topics related to the concepts of (1) self-discipline, (2) prayers for the frontliners, (3) total lockdown, (4) following government guidelines and protocols, and (5) flattening the curve of the disease. Meanwhile, word groups generated by Word2vec developed concepts such as (1) mass testing, (2) hope for faster recovery, and (3) expectation from the government. The average cosine similarity for word groups is 0.92, which implies strong relatedness of each word per group. This study proved that the use of NLP techniques helped in analyzing the themes of Facebook posts and comments related to the pandemic.