{ "data_id": "43697", "name": "covid-19_sentiments-India200320---310520", "exact_name": "covid-19_sentiments-India200320---310520", "version": 1, "version_label": "v1.0", "description": "About our Dataset\nThe journey of the collection of this Covid-19 India dataset begin with a competition where we have to do sentiment analysis of tweets. The data was collected from https:\/\/ieee-dataport.org\/open-access\/coronavirus-covid-19-tweets-dataset . This site gave us the tweet Id of relevant tweets and to extract the tweets text and other information, we used Hydrator app.\nAbout features of dataset\nThere are total 5 columns.\nColumn 1: 'Text ID'\n It contains unique ID for each tweet.\nColumn 2: 'Text'\n It is the tweet text of that particular tweet ID.\nColumn 3: 'Date'\n The date on which the tweet was tweeted.\nColumn 4: 'Location'\n The place from where the tweet was tweeted.\nColumn 5: 'Sentiments'\n The sentiment value of that tweet, whether it is positive, negative or neutral.\n If sentiment score is greater then 0 then sentiment is positive.\n If sentiment score is equal to 0 then sentiment is neutral.\n If sentiment score is less then 0 then sentiment is negative.\nAcknowledgements\nWe wouldn't be here without the help of others. We would like to acknowledge https:\/\/ieee-dataport.org\/open-access\/coronavirus-covid-19-tweets-dataset for providing the tweet Id's. We would also like to acknowledge Hydrator app for fectching tweets.\nInspiration\nActually we got the inspiration from the competition where we were given the task to categorize the sentiment values of COVID - 19 India tweets.", "format": "arff", "uploader": "Dustin Carrion", "uploader_id": 30123, "visibility": "public", "creator": null, "contributor": null, "date": "2022-03-24 07:15:52", "update_comment": null, "last_update": "2022-03-24 07:15:52", "licence": "Database: Open Database, Contents: Database Contents", "status": "active", "error_message": null, "url": "https:\/\/www.openml.org\/data\/download\/22102522\/dataset", "default_target_attribute": null, "row_id_attribute": null, "ignore_attribute": "\"Text_Id\"", "runs": 0, "suggest": { "input": [ "covid-19_sentiments-India200320---310520", "About our Dataset The journey of the collection of this Covid-19 India dataset begin with a competition where we have to do sentiment analysis of tweets. The data was collected from https:\/\/ieee-dataport.org\/open-access\/coronavirus-covid-19-tweets-dataset . This site gave us the tweet Id of relevant tweets and to extract the tweets text and other information, we used Hydrator app. About features of dataset There are total 5 columns. Column 1: 'Text ID' It contains unique ID for each tweet. Colum " ], "weight": 5 }, "qualities": { "NumberOfInstances": 648958, "NumberOfFeatures": 4, "NumberOfClasses": null, "NumberOfMissingValues": 10980, "NumberOfInstancesWithMissingValues": 10980, "NumberOfNumericFeatures": 1, "NumberOfSymbolicFeatures": 0, "Dimensionality": 6.163727082492241e-6, "PercentageOfNumericFeatures": 25, "MajorityClassPercentage": null, "PercentageOfSymbolicFeatures": 0, "MajorityClassSize": null, "MinorityClassPercentage": null, "MinorityClassSize": null, "NumberOfBinaryFeatures": 0, "PercentageOfBinaryFeatures": 0, "PercentageOfInstancesWithMissingValues": 1.6919430841441203, "AutoCorrelation": null, "PercentageOfMissingValues": 0.4229857710360301 }, "tags": [ { "uploader": "38960", "tag": "Computer Systems" } ], "features": [ { "name": "Text_Id", "index": "0", "type": "numeric", "distinct": "324045", "missing": "0", "ignore": "1", "min": "2147483647", "max": "2147483647", "mean": "2147483647", "stdev": "2147483647" }, { "name": "Text", "index": "1", "type": "string", "distinct": "161648", "missing": "0" }, { "name": "Date", "index": "2", "type": "string", "distinct": "172416", "missing": "0" }, { "name": "Location", "index": "3", "type": "string", "distinct": "8489", "missing": "0" }, { "name": "Sentiments", "index": "4", "type": "numeric", "distinct": "7508", "missing": "10980", "min": "-1", "max": "1", "mean": "0", "stdev": "0" } ], "nr_of_issues": 0, "nr_of_downvotes": 0, "nr_of_likes": 0, "nr_of_downloads": 0, "total_downloads": 0, "reach": 0, "reuse": 0, "impact_of_reuse": 0, "reach_of_reuse": 0, "impact": 0 }