- This document summarizes a project that analyzes over 2.4 million Latvian language tweets related to food and eating collected since 2011. - The tweets have been annotated for sentiment, named entities, and question-answer pairs and analyzed to identify trends, seasonal patterns for foods, and relationships between foods and senses of smell, taste, and temperature. - Key findings include identifying popular foods and drinks mentioned over time, seasonal patterns for foods like chocolate and berries, and changing sentiment toward foods like meat detected on Twitter. Publications and code related to the project are available on GitHub.

1. Tracing Multisensory Food
Experience on Twitter
Matīss Rikters
July 13, 2021

2. Outline
• Project overview
• Dataset collection, processing, annotation
• Sentiment analysis, named entity recognition, question answering
• Dataset analysis, aspects from cognitive science

3. Project overview
• Started as my bachelor’s thesis in 2011
• Has been running ever since with little disruptions
• https://tvitediens.tk/ - go check it out
• Has its own Twitter account https://twitter.com/Twitediens
• Every day it tweets
• 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 interact with it

4. Main keywords
taste lunch beet potato mandarin sweet
eat feast bun cabbage sauce mushroom
breakfast drink carrot candy pancake onion
dine treat chips sour cream dumpling chocolate
dinner nom vegetable cream soup gingerbread tea
bite appetite meat cake rice tomato
meal orange Hesburger drink salad grape
food apple coffee McDonald's ice cream strawberry

5. Dataset overview
https://github.com/Usprogis/Latvian-Twitter-Eater-Corpus
Domain-specific about food and eating written in Latvian
2.4M+ tweets
• ~5,500 + 744 tweets with manually annotated sentiment (positife, 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, like
• ~43,000 automatically aggregated question-answer tweet pairs
• ~155,000 tweets have images
• ~165,000 have location info

6. Data collection
• A script uses the Twitter streaming API to listen for any of the 363
pre-defined keywords
• Records the latest tweets in a large MySQL database for storage and a
separate database for displaying data from the last 3 months online
• At the beginning of each month a scheduled script converts data from
the database into JSON format for easier processing

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, but
experiments are still in progress with no outstanding results just yet

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

12. Relations to smell, taste and temperature

13. Relations to smell, taste and temperature

14. Fun fact – cold soup is very popular in Latvia

15. Relations to smell, taste and temperature
Food/Drink Pleasant smell Bad smell Food/Drink
Tea 598 156 Meat
Chocolate 408 96 Tea
Coffee 386 93 Fish
Gingerbread 293 67 Cheese
Tangerines 244 65 Garlic
Strawberries 227 57 Coffee
Apple 220 48 Potatoes
Meat 183 39 Egg
Potatoes 142 38 Onion
Pancakes 142 37 Chocolate

16. Question answering
• We performed a small-scale human evaluation results by asking
5 annotatorsto evaluate a random 10% of the evaluation set by
marking generated answers as either OK or not good (NG).
• The evaluators marked 46.40% of answers as OK.
• The evaluators had an overall agreement of 66.27% (Free-
marginal kappa 0.33), which indicatesmoderate agreement.

17. Dataset overview over time
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
0
100,000
200,000
300,000
400,000
500,000
600,000
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Twitter
Users
Tweets
Tweets Trend Twitter Instagram Twitter Users

18. Most popular foods and drinks
Food Count Drink Count
Chocolate 117,235 Tea 163,338
Ice cream 86,109 Coffee 120,040
Meat 85,574 Juice 18,179
Potatoes 70,135 Water 15,692
Salads 61,616 Beer 14,845
Cake 52,267 Cocktails 8,207
Soup 46,545 Coca-cola 5,016
Pancakes 40,203 Alcohol 4,766
Sauce 40,201 Champagne 3,673
Apple 36,571 Vodka 2,802

19. Trends and analysis
• Seasonal trends
• Large noticeable trends
• In-deapth look into specific foods

20. Large trends
32
Drastic price increase
65
Russia import ban
86
CoViD19 panic buying
2666
Horse meat burgers
0
500
1000
1500
2000
2500
0
10
20
30
40
50
60
70
80
90
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52
Meat
Tweet
Count
Butter,
Sprat,
Buckwheat
Tweet
Count
Week
Butter 2017 Sprats 2015 Salmon 2016 Buckwheat 2020 Meat 2013

21. Horse meat 2013

22. Seasonal trends
Jan Jan Feb Feb Mar Mar Apr Apr Apr May May Jun Jun Jul Jul Aug Aug Sep Sep Sep Oct Oct Nov Nov Dec Dec
0
50
100
150
200
250
300
350
0
100
200
300
400
500
600
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52
Ice
cream,
Strawberries
Chocolate,
Tangerines,
Gingerbread
Gingerbread Chocolate Tangerines Strawberries Ice cream

23. Timeline of tea tweets
0
20
40
60
80
10.2011
2.2012
6.2012
10.2012
2.2013
6.2013
10.2013
2.2014
6.2014
10.2014
2.2015
6.2015
10.2015
2.2016
6.2016
10.2016
2.2017
6.2017
10.2017
2.2018
6.2018
10.2018
2.2019
6.2019
10.2019
2.2020
6.2020
Tea
Green Tasty Sweet Bitter Black White

24. Timeline of coffee tweets
0
10
20
30
40
50
60
10.2011
12.2011
2.2012
4.2012
6.2012
8.2012
10.2012
12.2012
2.2013
4.2013
6.2013
8.2013
10.2013
12.2013
2.2014
4.2014
6.2014
8.2014
10.2014
12.2014
2.2015
4.2015
6.2015
8.2015
10.2015
12.2015
2.2016
4.2016
6.2016
8.2016
10.2016
12.2016
2.2017
4.2017
6.2017
8.2017
10.2017
12.2017
2.2018
4.2018
6.2018
8.2018
10.2018
12.2018
2.2019
4.2019
6.2019
8.2019
10.2019
12.2019
2.2020
4.2020
6.2020
8.2020
Coffee 2011-2020 Normalized
Black Tasty Sweet White Bitter Green

25. Pancakes during the week / time of day
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

26. Salad during the week / time of day
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

27. 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

28. 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. In review for International Journal of Food Design.

29. 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

30. End