Annotations for Exploring Food Tweets From Multiple Aspects
This research builds upon the Latvian Twitter Eater Corpus (LTEC), which is focused on the narrow domain of tweets related to food, drinks, eating and drinking. LTEC has been collected for more than 12 years and reaching almost 3 million tweets with the basic information as well as extended automatically and manually annotated metadata. In this paper we supplement the LTEC with manually annotated subsets of evaluation data for machine translation, named entity recognition, timeline-balanced sentiment analysis, and text-image relation classification. We experiment with each of the data sets using baseline models and highlight future challenges for various modelling approaches.
Code (1)
Tasks
Machine Translationnamed-entity-recognitionNamed Entity RecognitionRelation ClassificationSentiment AnalysisSimilar Papers 제목 키워드 기반
Fragmented and Valuable: Following Sentiment Changes in Food Tweets
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 …
Cultural Vocal Bursts Intensity PredictionRelationUtilizing Microblogs for Assisting Post-Disaster Relief Operations via Matching Resource Needs and Availabilities
During a disaster event, two types of information that are especially useful for coordinating relief operations are needs and availabilities of resources (e.g., food, water, medicines) in the affected region. Information…
UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection
Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodborne illness-related tweets for model tr…
Detecting Foodborne Illness Complaints in Multiple Languages Using English Annotations Only
Health departments have been deploying text classification systems for the early detection of foodborne illness complaints in social media documents such as Yelp restaurant reviews. Current systems have been successfully…
Machine Translationtext-classificationText ClassificationWhat Can We Learn From Almost a Decade of Food Tweets
We present the Latvian Twitter Eater Corpus - a set of tweets in the narrow domain related to food, drinks, eating and drinking. The corpus has been collected over time-span of over 8 years and includes over 2 million tw…
Question AnsweringSentiment Analysis