The use of sentiment and emotion analysis and data science to assess the language of nutrition-, food- and cooking-related content on social media: a systematic scoping review

Abstract

Social media data is rapidly evolving and accessible which presents opportunities for research. Data science techniques, such as sentiment or emotion analysis which analyse textual emotion, provide an opportunity to gather insight from social media. This paper describes a systematic scoping review of interdisciplinary evidence to explore how sentiment or emotion analysis methods alongside other data science methods have been used to examine nutrition, food and cooking social media content. A PRISMA search strategy was used to search nine electronic databases in November 2020 and January 2022. Of 7325 studies identified, 36 studies were selected from 17 countries, and content was analysed thematically and summarised in an evidence table. Studies were published between 2014-2022 and used data from seven different social media platforms (Twitter, YouTube, Instagram, Reddit, Pinterest, Sina Weibo and mixed platforms). Five themes of research were identified including Dietary patterns, Cooking and recipes, Diet and Health, Public Health and Nutrition and Food in general. Papers developed a sentiment or emotion analysis tool or used available open-source tools. Accuracy to predict sentiment ranged from 33.33% (open-source engine) to 98.53% (engine developed for the study). The average proportion of sentiment was 38.8% positive, 46.6% neutral and 28.0% negative. Additional data science techniques used included topic modelling and network analysis. Future research requires optimising data extraction processes from social media platforms, the use of interdisciplinary teams to develop suitable and accurate methods for the subject and the use of complementary methods to gather deeper insights into these complex data.

An Explainable Prediction for Dietary-Related Diseases via Language Models

“…This novel application of AI techniques in nutritional research aims not only to enrich our understanding of diet-related health outcomes but also to enhance the precision and personalization of dietary interventions. It is our hope that these methodological advancements will encourage further inquiry and innovation at the intersection of technology and nutrition, fostering a deeper, more holistic understanding of the factors that influence dietary choices and their health consequences [ 48 , 49 , 50 ].…”

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

“…to provide a nuanced understanding of food waste social media data. The importance of interdisciplinary teams has been emphasised in prior research, which found that data science and subject-specific experts should work together to ensure the methods are rigorous, accurate, and relevant to the field of interest [14]. Therefore, this exploratory and data-driven research aimed to analyse food waste discussions on Twitter from an interdisciplinary perspective to identify how food waste is discussed in one part of the exosystem and explore how the conversation evolves over time.…”

“…In contrast, these conversations can also be considered when a policy change is made to analyse how the public perceives it [13]. However, current social media research has been small-scale, including methods such as manual content analysis, which typically analyses less than 5000 posts [14] (n = 220 [15], n = 423 [16], and n = 1000 [17]) due to limitations such as the time taken to code the data manually [18,19]. Furthermore, the topics discussed on social media may differ from what a participant would openly share with a researcher in traditional research settings such as focus groups [20].…”

“…Topic mod-elling can also be performed through a variety of approaches, such as non-probabilistic techniques such as Latent Semantic Analysis and Non-Negative Matrix Factorisation and probabilistic techniques such as Latent Dirichlet Allocation and Probabilistic Latent Semantic Analysis [32]. NLP has been used in many disciplines to assess public opinion towards climate change [33][34][35][36], food and nutrition [14], product reviews [37], and reactions to advances in artificial intelligence [38] and monitor various health issues such as Mpox [39], COVID-19 [40][41][42], or public opinion towards vaccination [43,44]. Data science techniques have also been used to explore food supply chain optimisation [45].…”

Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study

“…Previous research on social media related to nutrition has largely focused on engagement (eg, likes, shares, and comments) on a small scale (between 9 social media profile pages and 736 social media posts) using manual analysis by topic experts [29][30][31] and has less frequently explored the breadth of the public's opinions and emotions expressed in social media posts. More recently, sentiment analysis tools, along with additional data science techniques such as topic modeling and social network analysis, were used to explore many nutrition-related topics on social media across 37 studies [32]. Using sentiment analysis alongside other NLP techniques enables researchers to gain a more in-depth understanding of large data sets such as those created in social media, thus providing further insights into potential implications for public health.…”