{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fragmented-and-valuable-following-sentiment","title":"Fragmented and Valuable: Following Sentiment Changes in Food Tweets","arxiv_id":"2106.04903","date":"2021-06-09","proceeding":null,"authors":["Maija Kāle","Matīss Rikters"],"abstract":"We analysed sentiment and frequencies related to smell, taste and temperature expressed by food tweets in the Latvian language. To get a better understanding of the role of smell, taste and temperature in the mental map of food associations, we looked at such categories as 'tasty' and 'healthy', which turned out to be mutually exclusive. By analysing the occurrence frequency of words associated with these categories, we discovered that food discourse overall was permeated by `tasty' while the category of 'healthy' was relatively small. Finally, we used the analysis of temporal dynamics to see if we can trace seasonality or other temporal aspects in smell, taste and temperature as reflected in food tweets. Understanding the composition of social media content with relation to smell, taste and temperature in food tweets allows us to develop our work further - on food culture/seasonality and its relation to temperature, on our limited capacity to express smell-related sentiments, and the lack of the paradigm of taste in discussing food healthiness.","url_abs":"https://arxiv.org/abs/2106.04903v1","url_pdf":"https://arxiv.org/pdf/2106.04903v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fragmented-and-valuable-following-sentiment","repo_url":"https://github.com/M4t1ss/sentiment-analysis-toolkit","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"culture","task_name":"Cultural Vocal Bursts Intensity Prediction"},{"task_slug":null,"task_name":"Relation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}