semvec/pypi-packages huggingface dataset:
twittersentiment 0.0.3
A python package for sentiment analysis written using pytorch framework
Requires Python: >=3.6

[![](https://img.shields.io/github/issues/shahules786/Twitter-Sentiment)]()
[![](https://img.shields.io/github/license/shahules786/Twitter-Sentiment)]()
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# Twitter Sentiment analyzer


<p align="center">
  <img src="https://user-images.githubusercontent.com/25312635/95116850-4d01ff80-0765-11eb-887d-c3fbcf3797d0.png" />
</p>


Sentiment analysis is the task of determining the sentiment of a given expression in natural language, It is essentially a multiclass text classification text where the given input text is classified into positive, neutral, or negative sentiment. But the number of classes can vary according to the nature of the training dataset. This project aims to build a sentiment analyzer specifically for twitter domain.


<p align="center">
  <img src="https://user-images.githubusercontent.com/25312635/94103308-f1c13a80-fe51-11ea-819e-def5948c479f.png" width="50%" />
</p>

## Why a Custom model for twitter domain?

Simply put, a Tweet is a message sent on Twitter. Most of the tweets do not follow normal English grammar and vocabulary mainly due to the limitation of the number of characters allowed in a tweet. This requires special care to yield better performance, hence this project.

## Install
`!pip install twittersentiment`

## Examples

- Using pretrained model

![basic](https://user-images.githubusercontent.com/25312635/96710969-71dbb100-13ba-11eb-9756-651384688a8b.gif)


- You can also train your own mode with custom dataset and your choice of word embedding, see [examples](https://github.com/shahules786/Twitter-Sentiment/blob/master/examples/basic.ipynb)
optdash 0.0.2
A third-party dashboard for optuna.

# optdash

**Optdash** is a third-party dashboard for optuna.

![README GIF](docs/images/optdash-readme.gif)

## Key features

* Simple usage
* Interactive visualization
* Minimal dependencies
    * Optdash depends solely on Optuna (and sklearn, which is an optional depedency of optuna).


## Quick start

```shell script
optdash your/db/url
```

The db url is the same with optuna, whose format follows [RFC-1738](https://tools.ietf.org/html/rfc1738.html).

## Install

### From pypi

```shell script
pip install optdash
```


### From source

```shell script
cd optdash/frontend
npm i
npm run build
cd ../../
python -m pip install .
```