semvec/pypi-packages huggingface dataset:
moot 0.0.1
Detecting emotions behind the text, pyemotion package will help you to understand the emotions in textual meassages.
License: MIT

# What is emotionpy?
Emotions are biological states 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?

#### Emotionpy 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.

## [Check Demo](https://pyemotion.herokuapp.com/)

Let's get experience with the library, test your multiple use cases on web app and check whether the library performs as your expectations level.
seqtag-keras 1.0.6
Easy to use BiLSTM+CRF sequence tagging for text.
License: MIT
Requires Python: >=3.5.0

# This repo is a fork of https://github.com/Hironsan/anago

**Updated with support for tf 2.0 and newer keras versions.**

This repo is a fork of https://github.com/Hironsan/anago and is modified to suit my needs. The module's name is changed so that it doesn't conflict with the installation of the original repo and so that I can push my changes to PyPi.

**Unless you know why are you using this, don't use this module. Use the excellent https://github.com/Hironsan/anago**

The purpose of creating a fork (and a separate module) instead of using the excellent anago is as follows.

1.  **Maintain compatibility with newer tf and keras versions.**
2. Anago's installation comes with ELMo, allennlp and some other stuff, which are not needed for the intended use cases of this repo (mainly DeepSegment).
3. It's not easy to include a git repo link as a requirement in setup.py while publishing to PyPi. So, this module is renamed to seqtag-keras.

**Full credit and thanks for the original implementation of this repo goes to https://github.com/Hironsan/ and https://github.com/chakki-works/ . **
emotionpyy 0.0.1
Detecting emotions behind the text, pyemotionpyy package will help you to understand the emotions in textual meassages.
License: MIT
Requires Python: >=3.6

# What is emotionpyy?
Emotions are biological states 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?

#### emotionpyy 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.

## [Check Demo](https://pyemotion.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.