Dataset Open Access

Past Written Texts Dataset

John Ellul; Marina Polycarpou


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  <identifier identifierType="DOI">10.5281/zenodo.2670061</identifier>
  <creators>
    <creator>
      <creatorName>John Ellul</creatorName>
      <affiliation>University of Patras</affiliation>
    </creator>
    <creator>
      <creatorName>Marina Polycarpou</creatorName>
      <affiliation>Materia Group Cyprus</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Past Written Texts Dataset</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>social media sensing</subject>
    <subject>sentiment analysis</subject>
    <subject>text-based sentiment analysis</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-05-07</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2670061</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.2670060</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The dataset consists of features extracted from older adults&amp;rsquo; text.&lt;/p&gt;

&lt;p&gt;The texts were written by the older person either in an electronic mean (eg. older e-mail), or in paper form and were transcribed by the project&amp;#39;s clinical nurses.&lt;/p&gt;

&lt;p&gt;The texts were then translated to English using the MyMemory service (https://mymemory.translated.net/), and a series of features were generated that can be used for sentiment analysis.&lt;/p&gt;

&lt;p&gt;The list of fields of this dataset is presented below:&lt;/p&gt;

&lt;p&gt;- &lt;strong&gt;Part_id&lt;/strong&gt;: The user ID, which should be a 4-digit number&lt;/p&gt;

&lt;p&gt;- &lt;strong&gt;Date&lt;/strong&gt;: The recording date, which follows the &amp;ldquo;DD-MM-YY&amp;rdquo; format (eg. 14 September 2017, is formatted as 14-09-17)&lt;/p&gt;

&lt;p&gt;- &lt;strong&gt;Clinical_visit&lt;/strong&gt;: As several clinical evaluations were performed to each older adult, this number shows for which clinical evaluation these measurements refer to&lt;/p&gt;

&lt;p&gt;- &lt;strong&gt;Transcript&lt;/strong&gt;: If the text was written by the older adult (0) or was transcribed by a nurse (1)&lt;/p&gt;

&lt;p&gt;- &lt;strong&gt;Language&lt;/strong&gt;: The original language of the text (0 = Greek)&lt;/p&gt;

&lt;p&gt;- &lt;strong&gt;Text_length, Number_of_sentences, Number_of_words, Number_of_words_per_sentence, Text_entropy&lt;/strong&gt;: Statistical Measures&lt;/p&gt;

&lt;p&gt;- &lt;strong&gt;Desc_image_ENG_sentiment, Desc_event_sentiment, Prev_text_ENG_sentiment&lt;/strong&gt;: Sentiment Analysis&lt;/p&gt;

&lt;p&gt;- &lt;strong&gt;Tf-XX&lt;/strong&gt;: Term frequency &amp;ndash; Inverse document frequency&lt;/p&gt;

&lt;p&gt;- &lt;strong&gt;Tf-pos-XX&lt;/strong&gt;: Part of Speech analysis, using tf-idf methodology&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/690140/">690140</awardNumber>
      <awardTitle>Sensing and predictive treatment of frailty and associated co-morbidities using advanced personalized patient models and advanced interventions</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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