Exploring Food Waste Conversations on Social Media: A Sentiment, Emotion, and Topic Analysis of Twitter Data

Eva L. Jenkins (), Dickson Lukose, Linda Brennan, Annika Molenaar and Tracy A. McCaffrey ()
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Eva L. Jenkins: Department of Nutrition, Dietetics and Food, Monash University, Level 1, 264 Ferntree Gully Road, Notting Hill 3168, Australia
Dickson Lukose: Tabcorp Holdings Ltd., Level 19, Tower 2, 727 Collins Street, Melbourne 3008, Australia
Linda Brennan: School of Media and Communication, RMIT University, 124 La Trobe St, Melbourne 3004, Australia
Annika Molenaar: Department of Nutrition, Dietetics and Food, Monash University, Level 1, 264 Ferntree Gully Road, Notting Hill 3168, Australia
Tracy A. McCaffrey: Department of Nutrition, Dietetics and Food, Monash University, Level 1, 264 Ferntree Gully Road, Notting Hill 3168, Australia

Sustainability, 2023, vol. 15, issue 18, 1-26

Abstract: Food waste is a complex issue requiring novel approaches to understand and identify areas that could be leveraged for food waste reduction. Data science techniques such as sentiment analysis, emotion analysis, and topic modelling could be used to explore big-picture themes of food waste discussions. This paper aimed to examine food waste discussions on Twitter and identify priority areas for future food waste communication campaigns and interventions. Australian tweets containing food-waste-related search terms were extracted from the Twitter Application Programming Interface from 2019–2021 and analysed using sentiment and emotion engines. Topic modelling was conducted using Latent Dirichlet Allocation. Engagement was calculated as the sum of likes, retweets, replies, and quotes. There were 39,449 tweets collected over three years. Tweets were mostly negative in sentiment and angry in emotion. The topic model identified 13 key topics such as eating to save food waste, morals, economics, and packaging. Engagement was higher for tweets with polarising sentiments and negative emotions. Overall, our interdisciplinary analysis highlighted the negative discourse surrounding food waste discussions and identified priority areas for food waste communication. Data science techniques should be used in the future to monitor public perceptions and understand priority areas for food waste reduction.

Keywords: social media; Twitter; food waste; sentiment analysis; emotion analysis; topic modelling; natural language processing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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