Mapping Digital Discourses On Bottled Drinking Water Using Nlp, Network Analysis, And Zero-Shot Classification
DOI:
https://doi.org/10.56956/2re8hr53Keywords:
Bottled drinking water, consumer perception, natural language processing, social media analysis, sustainabilityAbstract
This study aims to analyze public discourse on bottled drinking water using a multidimensional social media analytics approach. The study analyzed 531 valid public conversations collected from X/Twitter through the SocialX platform between January and April 2026. The final dataset resulted from a structured preprocessing procedure involving duplicate removal, relevance screening, and text normalization to ensure analytical quality. A multidimensional analytical framework integrating sentiment analysis, emotion analysis, text network analysis (TNA), social network analysis (SNA), trend analysis, and zero-shot classification was employed to examine public discourse from complementary perspectives. To enhance analytical reliability, automated classification outputs were supported by manual validation of a subset of the data. The findings indicate that public discourse was dominated by neutral sentiment and positive emotional expressions. While sentiment analysis showed that most conversations were informational and neutral in tone, zero-shot classification revealed a slightly positive overall perception because many neutral discussions implicitly reflected favorable evaluations of convenience, accessibility, and product usefulness. Network analysis further revealed a decentralized communication structure with multiple influential actors, whereas text network analysis identified dominant themes related to price, product quality, packaging, and sustainability. This study contributes to the consumer behavior, digital communication, and sustainability literature by demonstrating the value of integrating multiple computational social media analytics techniques to capture naturally occurring consumer discourse beyond conventional survey-based approaches. Nevertheless, the findings should be interpreted within the context of a single social media platform and a relatively short observation period.
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