| dc.description.abstract | Innovation is the imperative driving force of the Sustainable Development Goals (SDGs). However, inquiries into how global societies perceive the crucial role of innovation upon the progress of SDGs, and how the social public’s engagement and sentiment is expressed toward different dimensions of innovation within SDGs remain under-tapped. Since the engagement, the concerns, and the sentiment of social media users are completely different concepts. Therefore, in order to understand the SDGs innovation context from various perspectives, a hybrid social media mining approach is implemented on knowledge extracted from Twitter’s user-generated content to establish broader understanding on three major purposes: (1) disclosing the social media users’ engagement on innovation within each individual dimension of 17 SDGs; (2) identifying topics of social media users’ concerns, thereby (3) measuring the social media users’ sentiment beneath the explored topics of concern. This approach comprehensively tracks how social media users’ engagement, concerns, and sentiment upon a certain topic change in different year. Retrieved tweets addressing innovation within SDGs are assigned to 17 SDGs groups using a codebook implemented by Python Pandas; thereby, favorite tweets and viral tweets of the 17 SDGs groups are defined for the preliminary descriptive analysis of engagement. The subsequent engagement analysis using quadrant distribution examines the participation and reaction of social media users toward the innovation within each of the 17 SDGs in different years. The fundamental topical themes of innovation within SDGs are presented by the hashtags-based network analysis. Latent semantic analysis, a topic-modeling approach, extracts topics of concern from social media users’ tweets regarding SDGs innovation; whereby the sentiment underneath those explored topics of concern is discovered using Python-based Vader library. This research unveils the complexity in association between opinion patterns, engagement, and sentiment that is unprecedentedly addressed in previous research; and also brings attention to the potential pitfall of interpretation where different types of engagement analysis may highlight different angles of how social media users attend and react to a certain subject. The structured network linkage of hashtags is also found to corroborate the findings of topic analysis of tweets. The study suggests that engagement analysis and sentiment analysis should be conducted back-to-back wherever topic analysis is applied, thus enabling the in-depth exploration of the status quo of the investigated domain such as innovation within SDGs. | en_US |