diff options
| author | CoprDistGit <infra@openeuler.org> | 2023-04-10 15:17:22 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-04-10 15:17:22 +0000 |
| commit | f32d6fc5ba1bbfccfb44ad8f24f93e992b4394fb (patch) | |
| tree | 389fa407b4d33a749ce296e8e6ae707be43c91d4 | |
| parent | 16b34b79a184f5067b1e5e09cc64c6b41d024168 (diff) | |
automatic import of python-vadersentiment
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-vadersentiment.spec | 156 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 158 insertions, 0 deletions
@@ -0,0 +1 @@ +/vaderSentiment-3.3.2.tar.gz diff --git a/python-vadersentiment.spec b/python-vadersentiment.spec new file mode 100644 index 0000000..6cf0f05 --- /dev/null +++ b/python-vadersentiment.spec @@ -0,0 +1,156 @@ +%global _empty_manifest_terminate_build 0 +Name: python-vaderSentiment +Version: 3.3.2 +Release: 1 +Summary: VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains. +License: MIT License +URL: https://github.com/cjhutto/vaderSentiment +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/77/8c/4a48c10a50f750ae565e341e697d74a38075a3e43ff0df6f1ab72e186902/vaderSentiment-3.3.2.tar.gz +BuildArch: noarch + + +%description +
+ VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is *specifically attuned to sentiments expressed in social media*. It is fully open-sourced under the `[MIT License] <http://choosealicense.com/>`_ (we sincerely appreciate all attributions and readily accept most contributions, but please don't hold us liable).
+
+ **Citation Information**
+
+ If you use either the dataset or any of the VADER sentiment analysis tools (VADER sentiment lexicon or Python code for rule-based sentiment analysis engine) in your research, please cite the above paper. For example:
+
+ **Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.**
+
+
+ * Code examples are provided on the `[VADER GitHub Repo] <https://github.com/cjhutto/vaderSentiment>`_
+ * Details about the scoring are provided on the `[VADER GitHub Repo] <https://github.com/cjhutto/vaderSentiment>`_
+ * VADER has been ported to Java, JavaScript, PHP, Scala, C#, Rust, and Go (see details and links to these on the `[VADER GitHub Repo] <https://github.com/cjhutto/vaderSentiment>`_
+
+
+
+Keywords: vader,sentiment,analysis,opinion,mining,nlp,text,data,text analysis,opinion analysis,sentiment analysis,text mining,twitter sentiment,opinion mining,social media,twitter,social,media
+Platform: any
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Scientific/Engineering :: Information Analysis
+Classifier: Topic :: Text Processing :: Linguistic
+Classifier: Topic :: Text Processing :: General
+Description-Content-Type: text/x-rst
+ + +%package -n python3-vaderSentiment +Summary: VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains. +Provides: python-vaderSentiment +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-vaderSentiment +
+ VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is *specifically attuned to sentiments expressed in social media*. It is fully open-sourced under the `[MIT License] <http://choosealicense.com/>`_ (we sincerely appreciate all attributions and readily accept most contributions, but please don't hold us liable).
+
+ **Citation Information**
+
+ If you use either the dataset or any of the VADER sentiment analysis tools (VADER sentiment lexicon or Python code for rule-based sentiment analysis engine) in your research, please cite the above paper. For example:
+
+ **Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.**
+
+
+ * Code examples are provided on the `[VADER GitHub Repo] <https://github.com/cjhutto/vaderSentiment>`_
+ * Details about the scoring are provided on the `[VADER GitHub Repo] <https://github.com/cjhutto/vaderSentiment>`_
+ * VADER has been ported to Java, JavaScript, PHP, Scala, C#, Rust, and Go (see details and links to these on the `[VADER GitHub Repo] <https://github.com/cjhutto/vaderSentiment>`_
+
+
+
+Keywords: vader,sentiment,analysis,opinion,mining,nlp,text,data,text analysis,opinion analysis,sentiment analysis,text mining,twitter sentiment,opinion mining,social media,twitter,social,media
+Platform: any
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Scientific/Engineering :: Information Analysis
+Classifier: Topic :: Text Processing :: Linguistic
+Classifier: Topic :: Text Processing :: General
+Description-Content-Type: text/x-rst
+ + +%package help +Summary: Development documents and examples for vaderSentiment +Provides: python3-vaderSentiment-doc +%description help +
+ VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is *specifically attuned to sentiments expressed in social media*. It is fully open-sourced under the `[MIT License] <http://choosealicense.com/>`_ (we sincerely appreciate all attributions and readily accept most contributions, but please don't hold us liable).
+
+ **Citation Information**
+
+ If you use either the dataset or any of the VADER sentiment analysis tools (VADER sentiment lexicon or Python code for rule-based sentiment analysis engine) in your research, please cite the above paper. For example:
+
+ **Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.**
+
+
+ * Code examples are provided on the `[VADER GitHub Repo] <https://github.com/cjhutto/vaderSentiment>`_
+ * Details about the scoring are provided on the `[VADER GitHub Repo] <https://github.com/cjhutto/vaderSentiment>`_
+ * VADER has been ported to Java, JavaScript, PHP, Scala, C#, Rust, and Go (see details and links to these on the `[VADER GitHub Repo] <https://github.com/cjhutto/vaderSentiment>`_
+
+
+
+Keywords: vader,sentiment,analysis,opinion,mining,nlp,text,data,text analysis,opinion analysis,sentiment analysis,text mining,twitter sentiment,opinion mining,social media,twitter,social,media
+Platform: any
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Natural Language :: English
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Classifier: Topic :: Scientific/Engineering :: Information Analysis
+Classifier: Topic :: Text Processing :: Linguistic
+Classifier: Topic :: Text Processing :: General
+Description-Content-Type: text/x-rst
+ + +%prep +%autosetup -n vaderSentiment-3.3.2 + +%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-vaderSentiment -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 3.3.2-1 +- Package Spec generated @@ -0,0 +1 @@ +a04f43dfcc57119bcabaa12f356c8c40 vaderSentiment-3.3.2.tar.gz |