1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
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
* Fri Apr 21 2023 Python_Bot <Python_Bot@openeuler.org> - 3.3.2-1
- Package Spec generated
|
