%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] `_ (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] `_ * Details about the scoring are provided on the `[VADER GitHub Repo] `_ * VADER has been ported to Java, JavaScript, PHP, Scala, C#, Rust, and Go (see details and links to these on the `[VADER GitHub Repo] `_ 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] `_ (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] `_ * Details about the scoring are provided on the `[VADER GitHub Repo] `_ * VADER has been ported to Java, JavaScript, PHP, Scala, C#, Rust, and Go (see details and links to these on the `[VADER GitHub Repo] `_ 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] `_ (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] `_ * Details about the scoring are provided on the `[VADER GitHub Repo] `_ * VADER has been ported to Java, JavaScript, PHP, Scala, C#, Rust, and Go (see details and links to these on the `[VADER GitHub Repo] `_ 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 * Fri Apr 21 2023 Python_Bot - 3.3.2-1 - Package Spec generated