Dataset Open Access

Dataset of Video Comments of a Vision Video Classified by Their Relevance, Polarity, Intention, and Topic

Oliver Karras; Eklekta Kristo


MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nmm##2200000uu#4500</leader>
  <datafield tag="041" ind1=" " ind2=" ">
    <subfield code="a">eng</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">YouTube video comment</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Vision video</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Feedback</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Classification</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Requirments engineering</subfield>
  </datafield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Crowd</subfield>
  </datafield>
  <controlfield tag="005">20210608014900.0</controlfield>
  <controlfield tag="001">4698969</controlfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Leibniz University Hannover</subfield>
    <subfield code="a">Eklekta Kristo</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">1271753</subfield>
    <subfield code="z">md5:f098d33a9299af245ce67b7893295aa7</subfield>
    <subfield code="u">https://zenodo.org/record/4698969/files/Dataset.xlsx</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">47132</subfield>
    <subfield code="z">md5:142d884e1ff284a3ba3455d95abcc5c3</subfield>
    <subfield code="u">https://zenodo.org/record/4698969/files/Detailed analysis results.xlsx</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">73929</subfield>
    <subfield code="z">md5:9c8608d5af3a5c931ca3f7a260f89f6a</subfield>
    <subfield code="u">https://zenodo.org/record/4698969/files/Guide Sheet - Multi-class labeling.pdf</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">89689</subfield>
    <subfield code="z">md5:2b1a2cd29b7b5474f79587b0a68d24c1</subfield>
    <subfield code="u">https://zenodo.org/record/4698969/files/Guide Sheet - Single-class labeling.pdf</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">242050</subfield>
    <subfield code="z">md5:f0902352eaddb1b6c346b6c37e063c28</subfield>
    <subfield code="u">https://zenodo.org/record/4698969/files/Python scripts for analysis.zip</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2021-06-07</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire_data</subfield>
    <subfield code="o">oai:zenodo.org:4698969</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">TIB - Leibniz Information Centre for Science and Technology</subfield>
    <subfield code="0">(orcid)0000-0001-5336-6899</subfield>
    <subfield code="a">Oliver Karras</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Dataset of Video Comments of a Vision Video Classified by Their Relevance, Polarity, Intention, and Topic</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;This dataset contains all comments (comments and replies) of the YouTube vision video &lt;a href="https://www.youtube.com/watch?v=u5V_VzRrSBI"&gt;&amp;quot;Tunnels&amp;quot; by &amp;quot;The Boring Company&amp;quot;&lt;/a&gt; fetched on 2020-10-13 using YouTube API. The comments are classified manually by three persons. We performed a single-class labeling of the video comments regarding their relevance for requirement engineering (RE) (ham/spam), their polarity (positive/neutral/negative). Furthermore, we performed a multi-class labeling of the comments regarding their intention (feature request and problem report) and their topic (efficiency and safety). While a comment can only be relevant or not relevant and have only one polarity, a comment can have one or more intentions and also one or more topics.&lt;/p&gt;

&lt;p&gt;For the replies, one person also classified them regarding their relevance for RE. However, the investigation of the replies is ongoing and future work.&lt;/p&gt;

&lt;p&gt;Remark: For 126 comments and 26 replies, we could not determine the date and time since they were no longer accessible on YouTube at the time this data set was created. In the case of a missing date and time, we inserted &amp;quot;NULL&amp;quot; in the corresponding cell.&lt;/p&gt;

&lt;p&gt;This data set includes the following files:&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;&lt;a href="https://zenodo.org/api/files/732452e9-5a01-4ef9-a353-e505f5489b95/Dataset.xlsx"&gt;Dataset.xlsx &lt;/a&gt;contains the raw and labeled video comments and replies:

	&lt;ul&gt;
		&lt;li&gt;For each comment, the data set contains:
		&lt;ul&gt;
			&lt;li&gt;ID: An identification number generated by YouTube for the comment&lt;/li&gt;
			&lt;li&gt;Date: The date and time of the creation of the comment&lt;/li&gt;
			&lt;li&gt;Author: The username of the author of the comment&lt;/li&gt;
			&lt;li&gt;Likes: The number of likes of the comment&lt;/li&gt;
			&lt;li&gt;Replies: The number of replies to the comment&lt;/li&gt;
			&lt;li&gt;Comment: The written comment&lt;/li&gt;
			&lt;li&gt;Relevance: Label indicating the relevance of the comment for RE (ham = relevant, spam = irrelevant)&lt;/li&gt;
			&lt;li&gt;Polarity: Label indicating the polarity of the comment&lt;/li&gt;
			&lt;li&gt;Feature request: Label indicating that the comment request a feature&lt;/li&gt;
			&lt;li&gt;Problem report: Label indicating that the comment reports a problem&lt;/li&gt;
			&lt;li&gt;Efficiency: Label indicating that the comment deals with the topic efficiency&lt;/li&gt;
			&lt;li&gt;Safety: Label indicating that the comment deals with the topic safety&lt;/li&gt;
		&lt;/ul&gt;
		&lt;/li&gt;
		&lt;li&gt;For each reply, the data set contains:
		&lt;ul&gt;
			&lt;li&gt;ID: The identification number of the comment to which the reply belongs&lt;/li&gt;
			&lt;li&gt;Date: The date and time of the creation of the reply&lt;/li&gt;
			&lt;li&gt;Author: The username of the author of the reply&lt;/li&gt;
			&lt;li&gt;Likes: The number of likes of the reply&lt;/li&gt;
			&lt;li&gt;Comment: The written reply&lt;/li&gt;
			&lt;li&gt;Relevance: Label indicating the relevance of the reply for RE (ham = relevant, spam = irrelevant)&lt;/li&gt;
		&lt;/ul&gt;
		&lt;/li&gt;
	&lt;/ul&gt;
	&lt;/li&gt;
	&lt;li&gt;&lt;a href="https://zenodo.org/api/files/732452e9-5a01-4ef9-a353-e505f5489b95/Detailed%20analysis%20results.xlsx"&gt;Detailed analysis results.xlsx&lt;/a&gt; contains the detailed results of all ten times repeated 10-fold cross validation analyses for each of all considered combinations of machine learning algorithms and features&lt;/li&gt;
	&lt;li&gt;&lt;a href="https://zenodo.org/api/files/732452e9-5a01-4ef9-a353-e505f5489b95/Guide%20Sheet%20-%20Multi-class%20labeling.pdf"&gt;Guide Sheet - Multi-class labeling.pdf&lt;/a&gt; describes the coding task, defines the categories, and lists examples to reduce inconsistencies and increase the quality of manual multi-class labeling&lt;/li&gt;
	&lt;li&gt;&lt;a href="https://zenodo.org/api/files/732452e9-5a01-4ef9-a353-e505f5489b95/Guide%20Sheet%20-%20Single-class%20labeling.pdf"&gt;Guide Sheet - Single-class labeling.pdf&lt;/a&gt; describes the coding task, defines the categories, and lists examples to reduce inconsistencies and increase the quality of manual single-class labeling&lt;/li&gt;
	&lt;li&gt;&lt;a href="https://zenodo.org/api/files/732452e9-5a01-4ef9-a353-e505f5489b95/Python%20scripts%20for%20analysis.zip"&gt;Python scripts for analysis.zip&lt;/a&gt; contains the scripts (as jupyter notebooks) and prepared data (as csv-files) for the analyses&lt;/li&gt;
&lt;/ul&gt;</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.5281/zenodo.4533301</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5281/zenodo.4698969</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">dataset</subfield>
  </datafield>
</record>
51
38
views
downloads
All versions This version
Views 5110
Downloads 389
Data volume 13.8 MB665.4 kB
Unique views 385
Unique downloads 264

Share

Cite as