Originally posted by **JBGruber** June 7, 2024
I tried to parse a few complex PDFs, which worked really well. Now I put in a simpler one and was suprised to see that the result contains no whitespace. Not sure if I'm doing something wrong or if this might be a bug:
```python
import openparse
import urllib.request
urllib.request.urlretrieve("https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0207996&type=printable", "test.pdf")
basic_doc_path = "test.pdf"
parser = openparse.DocumentParser()
parsed_basic_doc = parser.parse(basic_doc_path)
print(parsed_basic_doc.nodes[2].text)
#> Abstract
Exploitinginformationinhealth-relatedsocialmediaservicesisofgreatinterestforpatients,
#> researchersandmedicalcompanies.Thechallengeis,however,toprovideeasy,quickand
#> relevantaccesstothevastamountofinformationthatisavailable.Onesteptowardsfacili-
#> tatinginformationaccesstoonlinehealthdataisopinionmining.Eventhoughtheclassifica-
#> tionofpatientopinionsintopositiveandnegativehasbeenpreviouslytackled,mostworks
#> makeuseofmachinelearningmethodsandbagsofwords.Ourfirstcontributionisanexten-
#> siveevaluationofdifferentfeatures,includinglexical,syntactic,semantic,network-based,
#> sentiment-basedandwordembeddingsfeaturestorepresentpatient-authoredtextsfor
#> polarityclassification.Thesecondcontributionofthisworkisthestudyofpolarfacts(i.e.
#> objectiveinformationwithpolarconnotations).Traditionally,thepresenceofpolarfactshas
#> beenneglectedandresearchinpolarityclassificationhasbeenboundedtoopinionated
#> texts.Wedemonstratetheexistenceandimportanceofpolarfactsforthepolarityclassifica-
#> tionofhealthinformation.
#> **Received:**January30,2018
```
Using copy and paste in a PDF reader, it looks like this:
> Exploiting information in health-related social media services is of great interest for patients,
> researchers and medical companies. The challenge is, however, to provide easy, quick and
> relevant access to the vast amount of information that is available. One step towards facili-
> tating information access to online health data is opinion mining. Even though the classifica-
> tion of patient opinions into positive and negative has been previously tackled, most works
> make use of machine learning methods and bags of words. Our first contribution is an exten-
> sive evaluation of different features, including lexical, syntactic, semantic, network-based,
> sentiment-based and word embeddings features to represent patient-authored texts for
> polarity classification. The second contribution of this work is the study of polar facts (i.e.
> objective information with polar connotations). Traditionally, the presence of polar facts has
> been neglected and research in polarity classification has been bounded to opinionated
> texts. We demonstrate the existence and importance of polar facts for the polarity classifica-
> tion of health information.
Discussed in https://github.com/Filimoa/open-parse/discussions/49
a1111111111 #> a1111111111 #> a1111111111 #> a1111111111 #> a1111111111
**Introduction**
Exploitinginformationinhealth-relatedsocialmediaservicesisofgreatinterestforpatients, #> researchersandmedicalcompanies.Thechallengeis,however,toprovideeasy,quickand #> relevantaccesstothevastamountofinformationthatisavailable.Onesteptowardsfacili- #> tatinginformationaccesstoonlinehealthdataisopinionmining.Eventhoughtheclassifica- #> tionofpatientopinionsintopositiveandnegativehasbeenpreviouslytackled,mostworks #> makeuseofmachinelearningmethodsandbagsofwords.Ourfirstcontributionisanexten- #> siveevaluationofdifferentfeatures,includinglexical,syntactic,semantic,network-based, #> sentiment-basedandwordembeddingsfeaturestorepresentpatient-authoredtextsfor #> polarityclassification.Thesecondcontributionofthisworkisthestudyofpolarfacts(i.e. #> objectiveinformationwithpolarconnotations).Traditionally,thepresenceofpolarfactshas #> beenneglectedandresearchinpolarityclassificationhasbeenboundedtoopinionated #> texts.Wedemonstratetheexistenceandimportanceofpolarfactsforthepolarityclassifica- #> tionofhealthinformation. #> **Received:**January30,2018 ``` Using copy and paste in a PDF reader, it looks like this: > Exploiting information in health-related social media services is of great interest for patients, > researchers and medical companies. The challenge is, however, to provide easy, quick and > relevant access to the vast amount of information that is available. One step towards facili- > tating information access to online health data is opinion mining. Even though the classifica- > tion of patient opinions into positive and negative has been previously tackled, most works > make use of machine learning methods and bags of words. Our first contribution is an exten- > sive evaluation of different features, including lexical, syntactic, semantic, network-based, > sentiment-based and word embeddings features to represent patient-authored texts for > polarity classification. The second contribution of this work is the study of polar facts (i.e. > objective information with polar connotations). Traditionally, the presence of polar facts has > been neglected and research in polarity classification has been bounded to opinionated > texts. We demonstrate the existence and importance of polar facts for the polarity classifica- > tion of health information.