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
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
|
%global _empty_manifest_terminate_build 0
Name: python-text2emotion
Version: 0.0.5
Release: 1
Summary: Detecting emotions behind the text, text2emotion package will help you to understand the emotions in textual meassages.
License: MIT
URL: https://github.com/aman2656/text2emotion-library
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b0/47/c63fd37b02ebfdfaf7aa365d40b2ac357814a820b692dc1a652a6cbc8964/text2emotion-0.0.5.tar.gz
BuildArch: noarch
Requires: python3-nltk
Requires: python3-emoji
%description
# What is emotion?
Emotion is a biological state associated with the nervous system brought on by neurophysiological changes variously associated with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure.
(Source: Wikipedia)
Human being can easily identify the emotions from text and experience it. But what about the machines, are they able to identify the emotions from text?
#### text2emotion is the python package which will help you to extract the emotions from the content.
- Processes any textual message and recognize the emotions embedded in it.
- Compatible with 5 different emotion categories as Happy, Angry, Sad, Surprise and Fear.
## Features
> ##### 1. Text Pre-processing
> At first we have the major goal to perform data cleaning and make the content suitable for emotion analysis.
> - Remove the unwanted textual part from the message.
> - Perform the natural language processing techniques.
> - Bring out the well pre-processed text from the text pre-processing.
> ##### 2. Emotion Investigation
> Detect emotion from every word that we got from pre-processed text and take a count of it for further analytical process.
> - Find the appropriate words that express emotions or feelings.
> - Check the emotion category of each word.
> - Store the count of emotions relevant to the words found.
> ##### 3. Emotion Analysis
> After emotion investigation, there is the time of getting the significant output for the textual message we input earlier.
> - The output will be in the form of dictionary.
> - There will be keys as emotion categories and values as emotion score.
> - Higher the score of a particular emotion category, we can conclude that the message belongs to that category.
## How to use?
#### [Check Demo on Colab](https://bit.ly/3hlXujZ)
## App Deployment
Here's the code implementation with **Streamlit App** for the users.
1. Enter the text.
2. Hit the submit button.
3. Tada!! Get the output in visual form.
#### [Check Demo of App](https://text2emotion.herokuapp.com/)
Let's experience the library, test your multiple use cases on web app and check whether the library performs as per your expectations.
%package -n python3-text2emotion
Summary: Detecting emotions behind the text, text2emotion package will help you to understand the emotions in textual meassages.
Provides: python-text2emotion
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-text2emotion
# What is emotion?
Emotion is a biological state associated with the nervous system brought on by neurophysiological changes variously associated with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure.
(Source: Wikipedia)
Human being can easily identify the emotions from text and experience it. But what about the machines, are they able to identify the emotions from text?
#### text2emotion is the python package which will help you to extract the emotions from the content.
- Processes any textual message and recognize the emotions embedded in it.
- Compatible with 5 different emotion categories as Happy, Angry, Sad, Surprise and Fear.
## Features
> ##### 1. Text Pre-processing
> At first we have the major goal to perform data cleaning and make the content suitable for emotion analysis.
> - Remove the unwanted textual part from the message.
> - Perform the natural language processing techniques.
> - Bring out the well pre-processed text from the text pre-processing.
> ##### 2. Emotion Investigation
> Detect emotion from every word that we got from pre-processed text and take a count of it for further analytical process.
> - Find the appropriate words that express emotions or feelings.
> - Check the emotion category of each word.
> - Store the count of emotions relevant to the words found.
> ##### 3. Emotion Analysis
> After emotion investigation, there is the time of getting the significant output for the textual message we input earlier.
> - The output will be in the form of dictionary.
> - There will be keys as emotion categories and values as emotion score.
> - Higher the score of a particular emotion category, we can conclude that the message belongs to that category.
## How to use?
#### [Check Demo on Colab](https://bit.ly/3hlXujZ)
## App Deployment
Here's the code implementation with **Streamlit App** for the users.
1. Enter the text.
2. Hit the submit button.
3. Tada!! Get the output in visual form.
#### [Check Demo of App](https://text2emotion.herokuapp.com/)
Let's experience the library, test your multiple use cases on web app and check whether the library performs as per your expectations.
%package help
Summary: Development documents and examples for text2emotion
Provides: python3-text2emotion-doc
%description help
# What is emotion?
Emotion is a biological state associated with the nervous system brought on by neurophysiological changes variously associated with thoughts, feelings, behavioural responses, and a degree of pleasure or displeasure.
(Source: Wikipedia)
Human being can easily identify the emotions from text and experience it. But what about the machines, are they able to identify the emotions from text?
#### text2emotion is the python package which will help you to extract the emotions from the content.
- Processes any textual message and recognize the emotions embedded in it.
- Compatible with 5 different emotion categories as Happy, Angry, Sad, Surprise and Fear.
## Features
> ##### 1. Text Pre-processing
> At first we have the major goal to perform data cleaning and make the content suitable for emotion analysis.
> - Remove the unwanted textual part from the message.
> - Perform the natural language processing techniques.
> - Bring out the well pre-processed text from the text pre-processing.
> ##### 2. Emotion Investigation
> Detect emotion from every word that we got from pre-processed text and take a count of it for further analytical process.
> - Find the appropriate words that express emotions or feelings.
> - Check the emotion category of each word.
> - Store the count of emotions relevant to the words found.
> ##### 3. Emotion Analysis
> After emotion investigation, there is the time of getting the significant output for the textual message we input earlier.
> - The output will be in the form of dictionary.
> - There will be keys as emotion categories and values as emotion score.
> - Higher the score of a particular emotion category, we can conclude that the message belongs to that category.
## How to use?
#### [Check Demo on Colab](https://bit.ly/3hlXujZ)
## App Deployment
Here's the code implementation with **Streamlit App** for the users.
1. Enter the text.
2. Hit the submit button.
3. Tada!! Get the output in visual form.
#### [Check Demo of App](https://text2emotion.herokuapp.com/)
Let's experience the library, test your multiple use cases on web app and check whether the library performs as per your expectations.
%prep
%autosetup -n text2emotion-0.0.5
%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-text2emotion -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.5-1
- Package Spec generated
|
