Subject: EducationSpecific: Online Learning and FunType: Questionnaire survey data (csv / excel)Date: February - March 2020Content: Students' views about online learning and fun Data Source: Project OLAFValue: These data provide students' beliefs about how learning occurs and correlations with fun. Participants were 206 students from the OU

This dataset provides information about the number of properties, residents, and average property values for Funny Bird Lane cross streets in Buffalo, WY.

This is the task dataset for SemEval-2020 Task 7: Assessing Humor in Edited News Headlines.

The task’s dataset contains news headlines in which short edits were applied to make them funny, and the funniness of these edited headlines was rated using crowdsourcing. This task includes two subtasks, the first of which is to estimate the funniness of headlines on a humor scale in the interval 0-3. The second subtask is to predict, for a pair of edited versions of the same original headline, which is the funnier version.

CodaLab page hosting the competition: https://competitions.codalab.org/competitions/20970

Starter Github code (scripts for running baseline and evaluation): https://github.com/n-hossain/semeval-2020-task-7-humicroedit

Task mailing list:

https://groups.google.com/forum/#!forum/semeval-2020-task-7-all

ZIP contents:

Folders: - subtask-1: Dataset for the funniness regression subtask. - subtask-2: Dataset for the "Funnier of the Two" classification subtask.

Files: - {train, dev, test}.csv: the task's dataset including labels - train_funlines.csv: additional training data gathered from the FunLines competition (https://funlines.co) - baseline.zip: contains csv file which is the output of the BASELINE system. This is a template of the output format that can be submitted to CodaLab for scoring.

Reference

Please cite the task paper when using this dataset:

Nabil Hossain, John Krumm, Michael Gamon and Henry Kautz. 2020. Semeval-2020 Task 7: Assessing Humor in Edited News Headlines. In Proceedings of International Workshop on Semantic Evaluation (SemEval-2020).

BIBTEX: @InProceedings{hossainSemEval2020Task7, author = {Hossain, Nabil and Krumm, John and Gamon, Michael and Kautz,Henry}, title = {SemEval-2020 {T}ask 7: {A}ssessing Humor in Edited News Headlines}, booktitle = {Proceedings of the 14th International Workshop on Semantic Evaluation ({S}em{E}val-2020)}, address = {Barcelona, Spain}, year = {2020}}

Dataset Card for Funny Insults

  Dataset Summary

This dataset contains 1,702 humorous insults collected from funny-insults.com. The dataset includes the insult text, categorization information, and user-provided ratings for each entry. This collection represents a variety of comedic insults across different categories that can be used for humor analysis, text generation, or sentiment analysis tasks.

  Languages

The dataset is monolingual:

English (en): All insult… See the full description on the dataset page: https://huggingface.co/datasets/nyuuzyou/funnyinsults.

This dataset is about books. It has 1 row and is filtered where the book is Seriously funny Q&A : questions and answers from the Seriously Funny tour. It features 7 columns including author, publication date, language, and book publisher.

Dataset

This dataset was created by leanseventeen

Contents

This dataset provides information about the number of properties, residents, and average property values for Funny Cide Way cross streets in Leonardtown, MD.

This dataset is developed for the CELSA research project 'Humour and Conflict in the Public Sphere: Communication styles, humour controversies and contested freedoms in contemporary Europe'. The project sets out to conduct an interdisciplinary analysis of the interrelatedness between digital humor and social conflict. The dataset contains data for 550 items of digitally mediated humor (e.g. online memes, cartoons, video's, posts) created in the context of specific cases of socio-political conflict in four European countries (i.e. Belgium, Belarus, Estonia and Poland). The dataset offers coding of linguistic markers such as genre, humor mechanisms and communication style as well as a mapping of the discourse which the humorous items spark on social media. Here, comments made as a reactions to the humor on social media platforms are coded for types of response (e.g. positive, negative, humorous, non-humorous) as well as the incidence of meta-comments (comments on comments) and other linguistic metrics for analysis (e.g. types of speech used in audience reactions). The data was coded indepentently by four researchers with a background in each respective country in 2023-2024. This dataset can be used, for example, to analyse audience reception of digitally mediated humor, or allow the (cross-national) analysis of the impact of different humoristic genres in digital public spheres.

This work is done by Rochester Human-Computer Interaction (ROC HCI) lab, University of Rochester, USA with the collaboration of Language Technologies Institute, SCS, CMU, USA.

ROC-HCI Website: (https://roc-hci.com/)

This repository includes the UR-FUNNY dataset: first dataset for multimodal humor detection .

Please read the folllwoing paper for the details of the dataset and models. If you use the data and models, please consider citing the research paper:

@inproceedings{hasan-etal-2019-ur,
  title = "{UR}-{FUNNY}: A Multimodal Language Dataset for Understanding Humor",
  author = "Hasan, Md Kamrul and
   Rahman, Wasifur and
   Bagher Zadeh, AmirAli and
   Zhong, Jianyuan and
   Tanveer, Md Iftekhar and
   Morency, Louis-Philippe and
   Hoque, Mohammed (Ehsan)",
  booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
  month = nov,
  year = "2019",
  address = "Hong Kong, China",
  publisher = "Association for Computational Linguistics",
  url = "https://www.aclweb.org/anthology/D19-1211",
  doi = "10.18653/v1/D19-1211",
  pages = "2046--2056",
  abstract = "",
}

There are six pickle files in the extracted features folder:

1.data_folds 2.langauge_sdk 3.openface_features_sdk 4.covarep_features_sdk 5.humor_label_sdk 6.word_embedding_list

Data folds: data_folds.pkl has the dictionary that contains train, dev and test list of humor/not humor video segments id. These folds are speaker independent and homogenous. Please use these folds for valid comparison.

Langauge Features: word_embedding_list.pkl has the list of word embeddings of all unique words that are present in the UR-FUNNY dataset. We use the word indexes from this list as language feature. These word indexes are used to retrive the glove embeddings of the corresposnding words. We followed this approach to reduce the space. Because same word appears multiple times.

language_sdk.pkl contains a dictionary. The keys of the dictionary are the unique id of each humor / not humor video utterance. The corresponding raw video uttereances are also named by these unique id's.

The structure of the dictionary:

langauge_sdk{
  id1: {
    punchline_embedding_indexes : [ idx1,idx2,.... ]
    context_embedding_indexes : [[ idx2,idx30,.... ],[idx5,idx6......],..] 
    punchline_sentence : [....]
    context_sentences : [[sen1], [sen2],...]
    punchline_intervals : [ intervals of words in punchline ]
    context_intervals : [[ intervals of words in sen1 ], [ intervals of words in sen2 ],.......]
    }
  id2: {
    punchline_embedding_indexes : [ idx10,idx12,.... ]
    context_embedding_indexes : [[ idx21,idx4,.... ],[idx91,idx100......],..]  
    punchline_sentence : [....]
    context_sentences : [[sen1], [sen2],...]
    punchline_intervals : [ intervals of words in punchline ]
    context_intervals : [[ intervals of words in sen1 ], [ intervals of words in sen2 ],.......]         
    }
  .....
  .....
}

Each video segments has four kind of features:

1.punchline_features: It contanis the list of word indexes (described above) of punchline sentence. We will use this word index to retrive the word embedding (glove.840B.300d) from word_embedding_list (described above). So if the punchline has n words then the dimension will be n.

2.context_features: It contanis the list of word indexes for the sentences in context. It is two dimensional list. First dimension is the number of sentences in context. Second dimension is the number of word for each sentence.

3.punchline_sentence: It contains the punchline sentence

4.context_sentences: It contanis the sentences used in context

Acoustic Features: covarep_features_sdk.pkl contains a dictionary. The keys of the dictionary are the unique id of each humor / not humor video utterance. We used COVAREP (https://covarep.github.io/covarep/) to extract acoustic features. See the extracted_features.txt for the names of the features.

The structure of the covarep_features_sdk:

covarep_features_sdk{
  id1: {
    punchline_features : [ [ .... ],[ .... ], ...]
    context_features : [ [[ .... ],[......],..], [[ .... ],[......],..], ... ]               ....
    }

  id2:{
    punchline_features : [ [ .... ],[ .... ], ...]
    context_features : [ [[ .... ],[......],..], [[ .... ],[......],..], ... ]
    ....
  }
  ....
  ....
}

Each humor/not humor video segment has two kind of features:

1.punchline_features: It contanis the average covarep features for each word in the punchline sentence. We aligned our features on word level. The dimension of covarep fetaures is 81. So if the punchline has n words then the dimension will be n * 81.

2.context_features: It contanis the average covarep features for each word in the context sentences. It is a three dimen...

This dataset is about books. It has 7 rows and is filtered where the book is Funny business. It features 7 columns including author, publication date, language, and book publisher.

This dataset is about book series. It has 1 row and is filtered where the books is Funny beasts. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

RwanAshraf/humor-labeled-data dataset hosted on Hugging Face and contributed by the HF Datasets community

These are the data sets for a study on audience segmentation to predict receptivity to humorous persuasive messages

This dataset provides information about the number of properties, residents, and average property values for Funny Farm Road cross streets in Graham, NC.

This dataset provides information about the number of properties, residents, and average property values for Funny Cide Drive cross streets in Waxhaw, NC.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Dataset

This dataset was created by xingwzeng

Released under CC0: Public Domain

Contents

Objectives: To gather pilot data on the use of humor in the raising of children. Methodology: We developed and field-tested a 10-item survey to measure people’s experiences being raised with humor, and their views regarding humor as a parenting tool. Responses were aggregated into Disagree, Indeterminate, and Agree, and analyzed using standard statistical methods. Results: Of the 312 respondents, most identified as male (63.6%) and white (76.6%); and 11.3% reported being 18-25 years old, 49.4% 26-35 years old, and 39.4% 36-45 years old. The majority reported that: the people who raised them used humor in their parenting (55.2%); humor could be an effective parenting tool (71.8%); humor as a parenting tool has more potential benefit than harm (63.3%); they either use or plan to use humor in parenting their own children (61.8%); and they would value a course on how to utilize humor in parenting (69.7%). Conclusions: In this pilot study, respondents of child-bearing/rearing age reported positive views about humor as a parenting tool.

This dataset is about books. It has 1 row and is filtered where the author is Funny Guy. It features 7 columns including author, publication date, language, and book publisher.

923 Global export shipment records of Toy Plastic Funny Toy Series with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.