Analyzing the Language of Food in Various Contexts

1. Thrifty Food Tweets on a Rainy Day:
Analysing the Language of Food
in Various Contexts
Maija Kāle, Matīss Rikters

2. Outline
• Project overview
• Dataset collection, processing, annotation
• Sentiment analysis, named entity recognition, question answering
• Dataset analysis, aspects from cognitive science
• Two recent papers
• What Food Do We Tweet about on a Rainy Day?
• Tweeting on an Empty Stomach: Unpacking Food Price Hikes and Inflation via Twitter

3. Project overview
• Started as my bachelor’s thesis in 2011
• Was running for years with little disruptions
• https://tvitediens.lv - go check it out
• Has its own Twitter account https://twitter.com/Twitediens
• Every day it tweeted
• 5 most mentioned foods of the last 24 hours
• 5 most active users of the last 24 hours
• A random recommendation for lunch
• Twitter users occasionally interacted with it
• Been facing difficulties after the recent Twitter
leadership change, on pause at the moment...

4. Main keywords
taste lunch beets potatoes mandarins sweet
eat feast buns cabbage sauce mushrooms
breakfast drink carrots candy pancake onions
dine treat chips sour cream dumpling chocolate
dinner nom vegetables cream soup gingerbread tea
bite appetite meat cake rice tomato
meal oranges Hesburger drink salad grapes
food apples coffee McDonald's ice cream strawberries
4

5. Dataset overview
https://github.com/Usprogis/Latvian-Twitter-Eater-Corpus
Domain-specific about food and eating written in Latvian
2.5M+ tweets
• ~5,500 + 744 tweets with manually annotated sentiment
(positive, neutral, negative) for training and testing
• 744 tweets with manually annotated named entity classes of person names,
locations, organizations, food and drinks, and miscellaneous named entities
• ~43,000 automatically aggregated question-answer tweet pairs
• ~155,000 tweets with images
• ~167,000 with location info
5

6. Yearly Data Distribution
0
20,000
40,000
60,000
80,000
100,000
120,000
0
100,000
200,000
300,000
400,000
500,000
600,000
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Twitter
Users
Tweets
Tweets Trend Twitter Users Twitter Instagram Tiktok

7. Data processing

8. Experiments
• Sentiment analysis – about 5,500 tweets annotated for training and
744 as a test dataset
• Named entity recognition – the same 744 tweets annotated with
place, person, food, time, and misc. entities
• Question answering – about 19,000 tweets that express questions
along with any replies to the tweets make up about 43,000 question-
answer tweet pairs
• Multimodal experiments – about 155,000 tweets have images,
experiments still in progress...

9. Sentiment analysis
Training Data TE MP MP+PE TE+MP TE+MP+RV+PE+NI TE+MP+RV+PE
Naive Bayes 53.21 43.32 45.72 56.55 59.63 58.02
Perceptron 53.07 52.67 53.47 57.87 57.33 58.27
Stemmed
Naive Bayes 53.74 46.39 50.67 58.16 60.56 61.23
Perceptron 56.67 53.73 54.13 60.00 56.93 57.73
Lemmas
Naive Bayes 53.88 45.45 49.60 56.42 58.42 59.63
Perceptron 54.41 51.07 53.07 57.35 56.95 56.95
Stemmed Lemmas
Naive Bayes 54.41 45.99 49.33 57.62 59.63 59.63
Perceptron 53.34 51.47 52.67 58.29 56.68 57.09
Multilingual BERT 68.32
Latvian BERT 74.06

10. How to determine sentiment?
It was difficult to agree upon sentiment of some tweets
Consider those:
• “Batars tak arī viņus ēda paļube tgd mums no 9 izlabos uz 3 :D”
• “Batars was also eating them and now our grades will be marked from 9 to 3 :D”
• “Ja vēlies pazaudēt pāris kilogramus, izrauj savus zobus! Tad arī turpmāk būs
grūti apēst parāk daudz”
• “If you want to lose weight, just pull out your teeth! Then it is going to be
difficult to eat too much”

11. Sentiment over time
10%
20%
30%
40%
50%
60%
Neutral Positive Negative

12. Relation to temperature and other senses
Cold soup is very popular in Latvia in summer

13. Day of week & time of day
Pancakes on weekend afternoons, evenings
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Morning 1,107 1,128 1,122 1,049 1,221 1,617 1,887
Afternoon 2,122 2,071 2,015 2,030 2,236 2,704 3,410
Evening 2,133 2,171 2,096 2,044 1,810 1,856 2,515
Night 615 603 609 601 588 583 668
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Morning 3,613 3,679 3,521 3,399 3,265 1,148 1,146
Afternoon 3,628 3,219 3,071 3,057 2,658 2,630 2,970
Evening 2,838 2,852 2,682 2,619 2,187 2,241 2,696
Night 923 904 908 883 776 678 899
Salads on weekday mornings, afternoons

14. Relation to weather
• ‘Weather people’ is a term used by Bakhshi (2014) to explain our
dependence on the weather for food choice and satisfaction
• The weather:
• significantly alters consumers’ mood and consequently - behaviour
• affects both the frequency and the content of feedback provided by
food consumers

15. Weather Data Availability
940
960
980
1000
1020
1040
1060
-35
15
65
115
165
Air
Pressure
Temperature,
Snowfall,
Precipitation
Precipitation Snowfall Air Pressure Temperature

16. Results
Product Rainy Windy Warm Cold
Tea 8.78% 6.64% 7.70% 10.08%
Coffee 6.59% 5.94% 6.77% 6.73%
Chocolate 4.83% 3.50% 4.56% 5.14%
Ice cream 3.05% 1.75% 4.04% 2.39%
Meat 4.20% 9.44% 4.38% 3.95%
Potatoes 3.16% 2.80% 3.42% 3.17%
Salad 2.19% 3.15% 2.14% 1.81%
Cake 2.77% 4.20% 2.85% 2.93%
Soup 2.44% 2.10% 2.63% 2.57%
Pancakes 2.16% 0.70% 2.07% 2.20%
Sauce 2.01% 0.70% 2.07% 1.65%
Apples 1.35% 1.75% 1.86% 1.24%
Dumplings 2.25% 1.05% 2.28% 2.12%
Chicken 1.75% 2.10% 1.85% 1.72%
Negative Neutral Positive
Cold 12.59% 37.25% 50.17%
Warm 13.20% 38.68% 48.12%
Windy 23.15% 48.40% 28.45%
Snowy 11.88% 36.06% 52.06%
Rainy 13.63% 38.64% 47.73%
High Pres 23.10% 48.26% 28.63%
Low Pres 12.63% 38.72% 48.65%
• From the ~167,000 tweets with location data
• ~68,000 from Riga
• ~9,000 from areas around Riga
• For more location-related tweets, we selected all
remaining tweets which mention Riga or any of its
surrounding areas (Mārupe, Ķekava, etc.) in any valid
inflected form, adding ~54,000 tweets
• Total amount for the analysis - 131,595 tweets
• For sentiment analysis We use the 5,420 annotated tweets to
fine-tune multilingual BERT for this task along with ∼20,000
sentiment-annotated Latvian tweets from other sources
• Reaching an accuracy of 74.06% on the 744 tweet test set from LTEC

17. Rising food costs
• Recent food inflation rates in the Baltic countries have been the
highest in the euro area, ranging from 12% to 19% year-on-year
• Global food prices increased by 31% from December 2019 to
December 2022, while in Latvia this increase was 39.8%
• Latvia’s GDP per capita in 2022 was around three quarters of
the EU average, meaning that food price increases have a
significant impact on overall household spending patterns

18. Price-related tweets in LTEC
2%
4%
6%
8%
10%
12%
14%
0
100
200
300
400
500
600
0
5
10
15
20
Yearly
Tweets
Thousands
Price
Tweets
Thousands
Yearly Tweets
Price Tweets
Word Count Word Count
to buy 45,702 spending 1,144
cheap 21,044 economics 1,000
to cost 10,751 income 989
price 10,460 expenses 337
money 8,340 finance 255
salary 6,558 inflation 255
expensive 5,521 currency 71
afford 1,683 economy 64
value 1,637 markup 52
costs 1,425 deflation 5
10%
30%
50%
70%
Neutral Positive

19. To buy
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
To Buy
Meat Chocolate Ice cream
Vegetables Cake

20. Work in progress

21. Some conclusions so far, more to come
Large scale social network data can be helpful for better understanding human and food
relationships and forming strategies and tactics for nudging for healthier (but not necessarily less
tasty) food behavior
By researching food related behavior on social media we can move from fragmented and valuable
data to a better understanding and knowledge of food choice and sentiment associated with it
Our research results reveal that negative sentiment expressed about meat in Twitter is rising
steadily, however, large part of neutral tweets remain
Neutrality has, however, sharply decreased with the beginning of Covid-19 pandemic
Recognising and understanding the impact of weather on food consumers and their affective
responses helps to explain the complexities

22. Publications
• Sproģis, U., Rikters, M. (2020). What Can We Learn From Almost a Decade of Food
Tweets. In The 9th Conference on Human Language Technologies - the Baltic Perspective.
• Kāle, M., Rikters, M. (2021). Fragmented and Valuable: Following Sentiment Changes in
Food Tweets. In Smell, Taste, and Temperature Interfaces. ACM CHI 2021 workshop.
• Kāle, M., Rikters, M., Šķilters, J. (2021). Tracing Multisensory Food Experience on
Twitter. International Journal of Food Design.
• Kāle, M., Rikters, M. (2023). What Food Do We Tweet about on a Rainy Day? 言語処理
学会第29回年次大会.
• Kāle, M., Rikters, M. (2023). Exploring the Sentiment of Latvian Twitter Food Posts in
Various Weather Conditions. Digital Humanities in the Nordic and Baltic Countries 2023
• Kāle, M., Rikters, M. (2023). Tweeting on an Empty Stomach: Unpacking Food Price
Hikes and Inflation via Twitter. Currently under review.

23. All on GitHub
• Website - https://github.com/M4t1ss/TwitEdiens
• Main corpus - https://github.com/Usprogis/Latvian-Twitter-Eater-Corpus
• NER corpus - https://github.com/RinaldsViksna/Latvian-food-NER-corpus
• Sentiment analysis - https://github.com/M4t1ss/sentiment-analysis-toolkit
• Processing scripts - https://github.com/M4t1ss/Latvian-Twitter-Eater-Corpus-
Processing

24. End