semvec/pypi-packages huggingface dataset:
motent 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.
tometa 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.
tomet 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.