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|
%global _empty_manifest_terminate_build 0
Name: python-spacytextblob
Version: 4.0.0
Release: 1
Summary: A TextBlob sentiment analysis pipeline component for spaCy.
License: MIT
URL: https://github.com/SamEdwardes/spacytextblob
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/e0/34/4a4adda6938af6b36752b38860e9d9d8380739235de1cfd07def155e78c6/spacytextblob-4.0.0.tar.gz
BuildArch: noarch
Requires: python3-textblob
Requires: python3-spacy
%description
# spacytextblob
[](https://badge.fury.io/py/spacytextblob)
[](https://github.com/SamEdwardes/spacytextblob/actions/workflows/pytest.yml)
[](https://app.netlify.com/sites/spacytextblob/deploys)
A TextBlob sentiment analysis pipeline component for spaCy.
- [Docs](https://spacytextblob.netlify.app/)
- [GitHub](https://github.com/SamEdwardes/spacytextblob)
- [PyPi](https://pypi.org/project/spacytextblob/)
## Table of Contents
- [Install](#install)
- [Quick Start](#quick-start)
- [Quick Reference](#quick-reference)
- [Reference and Attribution](#reference-and-attribution)
## Install
Install *spacytextblob* from PyPi.
```bash
pip install spacytextblob
```
TextBlob requires additional data to be downloaded before getting started.
```bash
python -m textblob.download_corpora
```
spaCy also requires that you download a model to get started.
```bash
python -m spacy download en_core_web_sm
```
## Quick Start
*spacytextblob* allows you to access all of the attributes created of the `textblob.TextBlob` class but within the spaCy framework. The code below will demonstrate how to use *spacytextblob* on a simple string.
```python
import spacy
from spacytextblob.spacytextblob import SpacyTextBlob
nlp = spacy.load('en_core_web_sm')
text = "I had a really horrible day. It was the worst day ever! But every now and then I have a really good day that makes me happy."
nlp.add_pipe("spacytextblob")
doc = nlp(text)
print(doc._.blob.polarity)
# -0.125
print(doc._.blob.subjectivity)
# 0.9
print(doc._.blob.sentiment_assessments.assessments)
# [(['really', 'horrible'], -1.0, 1.0, None), (['worst', '!'], -1.0, 1.0, None), (['really', 'good'], 0.7, 0.6000000000000001, None), (['happy'], 0.8, 1.0, None)]
```
In comparison, here is how the same code would look using `TextBlob`:
```python
from textblob import TextBlob
text = "I had a really horrible day. It was the worst day ever! But every now and then I have a really good day that makes me happy."
blob = TextBlob(text)
print(blob.sentiment_assessments.polarity)
# -0.125
print(blob.sentiment_assessments.subjectivity)
# 0.9
print(blob.sentiment_assessments.assessments)
# [(['really', 'horrible'], -1.0, 1.0, None), (['worst', '!'], -1.0, 1.0, None), (['really', 'good'], 0.7, 0.6000000000000001, None), (['happy'], 0.8, 1.0, None)]
```
## Quick Reference
*spacytextblob* performs sentiment analysis using the [TextBlob](https://textblob.readthedocs.io/en/dev/quickstart.html) library. Adding *spacytextblob* to a spaCy nlp pipeline creates a new extension attribute for the `Doc`, `Span`, and `Token` classes from spaCy.
- `Doc._.blob`
- `Span._.blob`
- `Token._.blob`
The `._.blob` attribute contains all of the methods and attributes that belong to the `textblob.TextBlob` class Some of the common methods and attributes include:
- **`._.blob.polarity`**: a float within the range [-1.0, 1.0].
- **`._.blob.subjectivity`**: a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective.
- **`._.blob.sentiment_assessments.assessments`**: a list of polarity and subjectivity scores for the assessed tokens.
See the [textblob docs](https://textblob.readthedocs.io/en/dev/api_reference.html#textblob.blob.TextBlob) for the complete listing of all attributes and methods that are available in `._.blob`.
## Reference and Attribution
- TextBlob
- [https://github.com/sloria/TextBlob](https://github.com/sloria/TextBlob)
- [https://textblob.readthedocs.io/en/latest/](https://textblob.readthedocs.io/en/latest/)
- negspaCy (for inspiration in writing pipeline and organizing repo)
- [https://github.com/jenojp/negspacy](https://github.com/jenojp/negspacy)
- spaCy custom components
- [https://spacy.io/usage/processing-pipelines#custom-components](https://spacy.io/usage/processing-pipelines#custom-components)
%package -n python3-spacytextblob
Summary: A TextBlob sentiment analysis pipeline component for spaCy.
Provides: python-spacytextblob
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-spacytextblob
# spacytextblob
[](https://badge.fury.io/py/spacytextblob)
[](https://github.com/SamEdwardes/spacytextblob/actions/workflows/pytest.yml)
[](https://app.netlify.com/sites/spacytextblob/deploys)
A TextBlob sentiment analysis pipeline component for spaCy.
- [Docs](https://spacytextblob.netlify.app/)
- [GitHub](https://github.com/SamEdwardes/spacytextblob)
- [PyPi](https://pypi.org/project/spacytextblob/)
## Table of Contents
- [Install](#install)
- [Quick Start](#quick-start)
- [Quick Reference](#quick-reference)
- [Reference and Attribution](#reference-and-attribution)
## Install
Install *spacytextblob* from PyPi.
```bash
pip install spacytextblob
```
TextBlob requires additional data to be downloaded before getting started.
```bash
python -m textblob.download_corpora
```
spaCy also requires that you download a model to get started.
```bash
python -m spacy download en_core_web_sm
```
## Quick Start
*spacytextblob* allows you to access all of the attributes created of the `textblob.TextBlob` class but within the spaCy framework. The code below will demonstrate how to use *spacytextblob* on a simple string.
```python
import spacy
from spacytextblob.spacytextblob import SpacyTextBlob
nlp = spacy.load('en_core_web_sm')
text = "I had a really horrible day. It was the worst day ever! But every now and then I have a really good day that makes me happy."
nlp.add_pipe("spacytextblob")
doc = nlp(text)
print(doc._.blob.polarity)
# -0.125
print(doc._.blob.subjectivity)
# 0.9
print(doc._.blob.sentiment_assessments.assessments)
# [(['really', 'horrible'], -1.0, 1.0, None), (['worst', '!'], -1.0, 1.0, None), (['really', 'good'], 0.7, 0.6000000000000001, None), (['happy'], 0.8, 1.0, None)]
```
In comparison, here is how the same code would look using `TextBlob`:
```python
from textblob import TextBlob
text = "I had a really horrible day. It was the worst day ever! But every now and then I have a really good day that makes me happy."
blob = TextBlob(text)
print(blob.sentiment_assessments.polarity)
# -0.125
print(blob.sentiment_assessments.subjectivity)
# 0.9
print(blob.sentiment_assessments.assessments)
# [(['really', 'horrible'], -1.0, 1.0, None), (['worst', '!'], -1.0, 1.0, None), (['really', 'good'], 0.7, 0.6000000000000001, None), (['happy'], 0.8, 1.0, None)]
```
## Quick Reference
*spacytextblob* performs sentiment analysis using the [TextBlob](https://textblob.readthedocs.io/en/dev/quickstart.html) library. Adding *spacytextblob* to a spaCy nlp pipeline creates a new extension attribute for the `Doc`, `Span`, and `Token` classes from spaCy.
- `Doc._.blob`
- `Span._.blob`
- `Token._.blob`
The `._.blob` attribute contains all of the methods and attributes that belong to the `textblob.TextBlob` class Some of the common methods and attributes include:
- **`._.blob.polarity`**: a float within the range [-1.0, 1.0].
- **`._.blob.subjectivity`**: a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective.
- **`._.blob.sentiment_assessments.assessments`**: a list of polarity and subjectivity scores for the assessed tokens.
See the [textblob docs](https://textblob.readthedocs.io/en/dev/api_reference.html#textblob.blob.TextBlob) for the complete listing of all attributes and methods that are available in `._.blob`.
## Reference and Attribution
- TextBlob
- [https://github.com/sloria/TextBlob](https://github.com/sloria/TextBlob)
- [https://textblob.readthedocs.io/en/latest/](https://textblob.readthedocs.io/en/latest/)
- negspaCy (for inspiration in writing pipeline and organizing repo)
- [https://github.com/jenojp/negspacy](https://github.com/jenojp/negspacy)
- spaCy custom components
- [https://spacy.io/usage/processing-pipelines#custom-components](https://spacy.io/usage/processing-pipelines#custom-components)
%package help
Summary: Development documents and examples for spacytextblob
Provides: python3-spacytextblob-doc
%description help
# spacytextblob
[](https://badge.fury.io/py/spacytextblob)
[](https://github.com/SamEdwardes/spacytextblob/actions/workflows/pytest.yml)
[](https://app.netlify.com/sites/spacytextblob/deploys)
A TextBlob sentiment analysis pipeline component for spaCy.
- [Docs](https://spacytextblob.netlify.app/)
- [GitHub](https://github.com/SamEdwardes/spacytextblob)
- [PyPi](https://pypi.org/project/spacytextblob/)
## Table of Contents
- [Install](#install)
- [Quick Start](#quick-start)
- [Quick Reference](#quick-reference)
- [Reference and Attribution](#reference-and-attribution)
## Install
Install *spacytextblob* from PyPi.
```bash
pip install spacytextblob
```
TextBlob requires additional data to be downloaded before getting started.
```bash
python -m textblob.download_corpora
```
spaCy also requires that you download a model to get started.
```bash
python -m spacy download en_core_web_sm
```
## Quick Start
*spacytextblob* allows you to access all of the attributes created of the `textblob.TextBlob` class but within the spaCy framework. The code below will demonstrate how to use *spacytextblob* on a simple string.
```python
import spacy
from spacytextblob.spacytextblob import SpacyTextBlob
nlp = spacy.load('en_core_web_sm')
text = "I had a really horrible day. It was the worst day ever! But every now and then I have a really good day that makes me happy."
nlp.add_pipe("spacytextblob")
doc = nlp(text)
print(doc._.blob.polarity)
# -0.125
print(doc._.blob.subjectivity)
# 0.9
print(doc._.blob.sentiment_assessments.assessments)
# [(['really', 'horrible'], -1.0, 1.0, None), (['worst', '!'], -1.0, 1.0, None), (['really', 'good'], 0.7, 0.6000000000000001, None), (['happy'], 0.8, 1.0, None)]
```
In comparison, here is how the same code would look using `TextBlob`:
```python
from textblob import TextBlob
text = "I had a really horrible day. It was the worst day ever! But every now and then I have a really good day that makes me happy."
blob = TextBlob(text)
print(blob.sentiment_assessments.polarity)
# -0.125
print(blob.sentiment_assessments.subjectivity)
# 0.9
print(blob.sentiment_assessments.assessments)
# [(['really', 'horrible'], -1.0, 1.0, None), (['worst', '!'], -1.0, 1.0, None), (['really', 'good'], 0.7, 0.6000000000000001, None), (['happy'], 0.8, 1.0, None)]
```
## Quick Reference
*spacytextblob* performs sentiment analysis using the [TextBlob](https://textblob.readthedocs.io/en/dev/quickstart.html) library. Adding *spacytextblob* to a spaCy nlp pipeline creates a new extension attribute for the `Doc`, `Span`, and `Token` classes from spaCy.
- `Doc._.blob`
- `Span._.blob`
- `Token._.blob`
The `._.blob` attribute contains all of the methods and attributes that belong to the `textblob.TextBlob` class Some of the common methods and attributes include:
- **`._.blob.polarity`**: a float within the range [-1.0, 1.0].
- **`._.blob.subjectivity`**: a float within the range [0.0, 1.0] where 0.0 is very objective and 1.0 is very subjective.
- **`._.blob.sentiment_assessments.assessments`**: a list of polarity and subjectivity scores for the assessed tokens.
See the [textblob docs](https://textblob.readthedocs.io/en/dev/api_reference.html#textblob.blob.TextBlob) for the complete listing of all attributes and methods that are available in `._.blob`.
## Reference and Attribution
- TextBlob
- [https://github.com/sloria/TextBlob](https://github.com/sloria/TextBlob)
- [https://textblob.readthedocs.io/en/latest/](https://textblob.readthedocs.io/en/latest/)
- negspaCy (for inspiration in writing pipeline and organizing repo)
- [https://github.com/jenojp/negspacy](https://github.com/jenojp/negspacy)
- spaCy custom components
- [https://spacy.io/usage/processing-pipelines#custom-components](https://spacy.io/usage/processing-pipelines#custom-components)
%prep
%autosetup -n spacytextblob-4.0.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-spacytextblob -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 4.0.0-1
- Package Spec generated
|
