%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 - 0.0.5-1 - Package Spec generated