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diff --git a/python-text2emotion.spec b/python-text2emotion.spec new file mode 100644 index 0000000..03b9e24 --- /dev/null +++ b/python-text2emotion.spec @@ -0,0 +1,197 @@ +%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 |