Datasets:

ofai
/

omp

{"posts_labeled": {"description": "The \u201cOne Million Posts\u201d corpus is an annotated data set consisting of\nuser comments posted to an Austrian newspaper website (in German language).\n\nDER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper\u2019s website,\nthere is a discussion section below each news article where readers engage in\nonline discussions. The data set contains a selection of user posts from the\n12 month time span from 2015-06-01 to 2016-05-31. There are 11,773 labeled and\n1,000,000 unlabeled posts in the data set. The labeled posts were annotated by\nprofessional forum moderators employed by the newspaper.\n\nThe data set contains the following data for each post:\n\n* Post ID\n* Article ID\n* Headline (max. 250 characters)\n* Main Body (max. 750 characters)\n* User ID (the user names used by the website have been re-mapped to new numeric IDs)\n* Time stamp\n* Parent post (replies give rise to tree-like discussion thread structures)\n* Status (online or deleted by a moderator)\n* Number of positive votes by other community members\n* Number of negative votes by other community members\n\nFor each article, the data set contains the following data:\n\n* Article ID\n* Publishing date\n* Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1)\n* Title\n* Body\n\nDetailed descriptions of the post selection and annotation procedures are given in the paper.\n\n## Annotated Categories\n\nPotentially undesirable content:\n\n* Sentiment (negative/neutral/positive)\n An important goal is to detect changes in the prevalent sentiment in a discussion, e.g.,\n the location within the fora and the point in time where a turn from positive/neutral\n sentiment to negative sentiment takes place.\n* Off-Topic (yes/no)\n Posts which digress too far from the topic of the corresponding article.\n* Inappropriate (yes/no)\n Swearwords, suggestive and obscene language, insults, threats etc.\n* Discriminating (yes/no)\n Racist, sexist, misogynistic, homophobic, antisemitic and other misanthropic content.\n\nNeutral content that requires a reaction:\n\n* Feedback (yes/no)\n Sometimes users ask questions or give feedback to the author of the article or the\n newspaper in general, which may require a reply/reaction.\n\nPotentially desirable content:\n\n* Personal Stories (yes/no)\n In certain fora, users are encouraged to share their personal stories, experiences,\n anecdotes etc. regarding the respective topic.\n* Arguments Used (yes/no)\n It is desirable for users to back their statements with rational argumentation,\n reasoning and sources.\n", "citation": "@InProceedings{Schabus2017,\n Author = {Dietmar Schabus and Marcin Skowron and Martin Trapp},\n Title = {One Million Posts: A Data Set of German Online Discussions},\n Booktitle = {Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)},\n Pages = {1241--1244},\n Year = {2017},\n Address = {Tokyo, Japan},\n Doi = {10.1145/3077136.3080711},\n Month = aug\n}\n", "homepage": "https://ofai.github.io/million-post-corpus/", "license": "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License", "features": {"ID_Post": {"dtype": "string", "id": null, "_type": "Value"}, "ID_Parent_Post": {"dtype": "string", "id": null, "_type": "Value"}, "ID_Article": {"dtype": "string", "id": null, "_type": "Value"}, "ID_User": {"dtype": "string", "id": null, "_type": "Value"}, "CreatedAt": {"dtype": "string", "id": null, "_type": "Value"}, "Status": {"dtype": "string", "id": null, "_type": "Value"}, "Headline": {"dtype": "string", "id": null, "_type": "Value"}, "Body": {"dtype": "string", "id": null, "_type": "Value"}, "PositiveVotes": {"dtype": "int32", "id": null, "_type": "Value"}, "NegativeVotes": {"dtype": "int32", "id": null, "_type": "Value"}, "Category": {"num_classes": 9, "names": ["ArgumentsUsed", "Discriminating", "Inappropriate", "OffTopic", "PersonalStories", "PossiblyFeedback", "SentimentNegative", "SentimentNeutral", "SentimentPositive"], "names_file": null, "id": null, "_type": "ClassLabel"}, "Value": {"dtype": "int32", "id": null, "_type": "Value"}, "Fold": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "omp", "config_name": "posts_labeled", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 13955964, "num_examples": 40567, "dataset_name": "omp"}}, "download_checksums": {"https://github.com/aseifert/million-post-corpus/raw/master/data/posts_labeled.csv.xz": {"num_bytes": 1329892, "checksum": "2d1cb6cd8fec07c5d378f9be889b355c1eb23b30e8fde2dcbb073cdad6f472ad"}}, "download_size": 1329892, "post_processing_size": null, "dataset_size": 13955964, "size_in_bytes": 15285856}, "posts_unlabeled": {"description": "The \u201cOne Million Posts\u201d corpus is an annotated data set consisting of\nuser comments posted to an Austrian newspaper website (in German language).\n\nDER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper\u2019s website,\nthere is a discussion section below each news article where readers engage in\nonline discussions. The data set contains a selection of user posts from the\n12 month time span from 2015-06-01 to 2016-05-31. There are 11,773 labeled and\n1,000,000 unlabeled posts in the data set. The labeled posts were annotated by\nprofessional forum moderators employed by the newspaper.\n\nThe data set contains the following data for each post:\n\n* Post ID\n* Article ID\n* Headline (max. 250 characters)\n* Main Body (max. 750 characters)\n* User ID (the user names used by the website have been re-mapped to new numeric IDs)\n* Time stamp\n* Parent post (replies give rise to tree-like discussion thread structures)\n* Status (online or deleted by a moderator)\n* Number of positive votes by other community members\n* Number of negative votes by other community members\n\nFor each article, the data set contains the following data:\n\n* Article ID\n* Publishing date\n* Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1)\n* Title\n* Body\n\nDetailed descriptions of the post selection and annotation procedures are given in the paper.\n\n## Annotated Categories\n\nPotentially undesirable content:\n\n* Sentiment (negative/neutral/positive)\n An important goal is to detect changes in the prevalent sentiment in a discussion, e.g.,\n the location within the fora and the point in time where a turn from positive/neutral\n sentiment to negative sentiment takes place.\n* Off-Topic (yes/no)\n Posts which digress too far from the topic of the corresponding article.\n* Inappropriate (yes/no)\n Swearwords, suggestive and obscene language, insults, threats etc.\n* Discriminating (yes/no)\n Racist, sexist, misogynistic, homophobic, antisemitic and other misanthropic content.\n\nNeutral content that requires a reaction:\n\n* Feedback (yes/no)\n Sometimes users ask questions or give feedback to the author of the article or the\n newspaper in general, which may require a reply/reaction.\n\nPotentially desirable content:\n\n* Personal Stories (yes/no)\n In certain fora, users are encouraged to share their personal stories, experiences,\n anecdotes etc. regarding the respective topic.\n* Arguments Used (yes/no)\n It is desirable for users to back their statements with rational argumentation,\n reasoning and sources.\n", "citation": "@InProceedings{Schabus2017,\n Author = {Dietmar Schabus and Marcin Skowron and Martin Trapp},\n Title = {One Million Posts: A Data Set of German Online Discussions},\n Booktitle = {Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)},\n Pages = {1241--1244},\n Year = {2017},\n Address = {Tokyo, Japan},\n Doi = {10.1145/3077136.3080711},\n Month = aug\n}\n", "homepage": "https://ofai.github.io/million-post-corpus/", "license": "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License", "features": {"ID_Post": {"dtype": "string", "id": null, "_type": "Value"}, "ID_Parent_Post": {"dtype": "string", "id": null, "_type": "Value"}, "ID_Article": {"dtype": "string", "id": null, "_type": "Value"}, "ID_User": {"dtype": "string", "id": null, "_type": "Value"}, "CreatedAt": {"dtype": "string", "id": null, "_type": "Value"}, "Status": {"dtype": "string", "id": null, "_type": "Value"}, "Headline": {"dtype": "string", "id": null, "_type": "Value"}, "Body": {"dtype": "string", "id": null, "_type": "Value"}, "PositiveVotes": {"dtype": "int32", "id": null, "_type": "Value"}, "NegativeVotes": {"dtype": "int32", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "omp", "config_name": "posts_unlabeled", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 305770324, "num_examples": 1000000, "dataset_name": "omp"}}, "download_checksums": {"https://github.com/aseifert/million-post-corpus/raw/master/data/posts_unlabeled.csv.xz": {"num_bytes": 79296188, "checksum": "433e80787abf587ecbd54756b3b200b57b7ef31041f19ba0bb8d2c5cc39cad65"}}, "download_size": 79296188, "post_processing_size": null, "dataset_size": 305770324, "size_in_bytes": 385066512}, "articles": {"description": "The \u201cOne Million Posts\u201d corpus is an annotated data set consisting of\nuser comments posted to an Austrian newspaper website (in German language).\n\nDER STANDARD is an Austrian daily broadsheet newspaper. On the newspaper\u2019s website,\nthere is a discussion section below each news article where readers engage in\nonline discussions. The data set contains a selection of user posts from the\n12 month time span from 2015-06-01 to 2016-05-31. There are 11,773 labeled and\n1,000,000 unlabeled posts in the data set. The labeled posts were annotated by\nprofessional forum moderators employed by the newspaper.\n\nThe data set contains the following data for each post:\n\n* Post ID\n* Article ID\n* Headline (max. 250 characters)\n* Main Body (max. 750 characters)\n* User ID (the user names used by the website have been re-mapped to new numeric IDs)\n* Time stamp\n* Parent post (replies give rise to tree-like discussion thread structures)\n* Status (online or deleted by a moderator)\n* Number of positive votes by other community members\n* Number of negative votes by other community members\n\nFor each article, the data set contains the following data:\n\n* Article ID\n* Publishing date\n* Topic Path (e.g.: Newsroom / Sports / Motorsports / Formula 1)\n* Title\n* Body\n\nDetailed descriptions of the post selection and annotation procedures are given in the paper.\n\n## Annotated Categories\n\nPotentially undesirable content:\n\n* Sentiment (negative/neutral/positive)\n An important goal is to detect changes in the prevalent sentiment in a discussion, e.g.,\n the location within the fora and the point in time where a turn from positive/neutral\n sentiment to negative sentiment takes place.\n* Off-Topic (yes/no)\n Posts which digress too far from the topic of the corresponding article.\n* Inappropriate (yes/no)\n Swearwords, suggestive and obscene language, insults, threats etc.\n* Discriminating (yes/no)\n Racist, sexist, misogynistic, homophobic, antisemitic and other misanthropic content.\n\nNeutral content that requires a reaction:\n\n* Feedback (yes/no)\n Sometimes users ask questions or give feedback to the author of the article or the\n newspaper in general, which may require a reply/reaction.\n\nPotentially desirable content:\n\n* Personal Stories (yes/no)\n In certain fora, users are encouraged to share their personal stories, experiences,\n anecdotes etc. regarding the respective topic.\n* Arguments Used (yes/no)\n It is desirable for users to back their statements with rational argumentation,\n reasoning and sources.\n", "citation": "@InProceedings{Schabus2017,\n Author = {Dietmar Schabus and Marcin Skowron and Martin Trapp},\n Title = {One Million Posts: A Data Set of German Online Discussions},\n Booktitle = {Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)},\n Pages = {1241--1244},\n Year = {2017},\n Address = {Tokyo, Japan},\n Doi = {10.1145/3077136.3080711},\n Month = aug\n}\n", "homepage": "https://ofai.github.io/million-post-corpus/", "license": "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License", "features": {"ID_Article": {"dtype": "string", "id": null, "_type": "Value"}, "Path": {"dtype": "string", "id": null, "_type": "Value"}, "publishingDate": {"dtype": "string", "id": null, "_type": "Value"}, "Title": {"dtype": "string", "id": null, "_type": "Value"}, "Body": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "omp", "config_name": "articles", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 43529400, "num_examples": 12087, "dataset_name": "omp"}}, "download_checksums": {"https://github.com/aseifert/million-post-corpus/raw/master/data/articles.csv.xz": {"num_bytes": 10681288, "checksum": "ff707a8adddd0f8785c7668b051d01b69cfac696db89afbd46054656f909a479"}}, "download_size": 10681288, "post_processing_size": null, "dataset_size": 43529400, "size_in_bytes": 54210688}}