1. Description

Hello guys, this is my small application used to predict comments are positive or negative.
I used selenium to crawl the entire data from Shopee Vietnam with over than 23.000 records. Each record consists mainly of the client's comment and his number of stars for the his comment.
After training phase and several model evaluation techniques, the model was embedded into web using Django framework.
The main field that my app concentrate on is fashion. So if you try to force the model predict on a wrong data (such as technology comments, food comments,...), The possibility is that the model can not predict accurately.
So, if you are interested in my application, you can read more the documents inside the directory Docs. I write with such a clarity and precision for what techniques I used.

2. Installation

Firstly, you need to create a new conda environment using python 3.8.10 and then activate the new environment which you have just created.
```
conda create --name sentiment python==3.8.10 pip
conda activate sentiment
```
OK, open your terminal at the same level with file requirements.txt to install some python packages.
```
pip install -r requirements.txt
```
And then, change your directory to web directory.
```
cd web
```
and now, you are standing at path .../SCL_KHVW_Sentiment_Analysis_Project/web/
Finally, you can run the Django application by the followed command.
```
python manage.py runserver
```
Because this application uses TensorFlow, SkLearn and some other heavy packages. So for the first time you run this app, it will take several seconds to load model and initialize some configuration, after that moment, the app can work smoothly.
OK, open your favorite browser and go to url http://localhost:8000, the super gorgeous interface of my app will appear like this 😅 (sorry front-end developers).

Run the followed command to run the application.

docker run --name sentiment-analysis -p 8000:8000 -d manhcuong8499/sentiment-analysis

Realtime video demo with so cute host: https://youtu.be/iQgCI3D1mdo
After you run the application successfully, enter your comment at the textarea component, then click the button Send and wait a bit to get the predicted result.
Let's try with some positive comments.
- With this comment, the model predict exactly. You can see predicted probability of positive value is higher than negative value, so really this is a positive comment.
- Some images below for other positive comments.
OK, let's try with some negative comments.
- Let leave only the thumbs-down emoji to see how the model will deal with.
- Some images below for other negative comments.
OK, cool. Thank you so much. Let clone or download as zip my application and run on your machine. See you 😍.

Name		Name	Last commit message	Last commit date
Latest commit History 119 Commits
Docs		Docs
data		data
images		images
legacy		legacy
models		models
modules		modules
tb_log_sentiment/models		tb_log_sentiment/models
web		web
.gitignore		.gitignore
00.intro_scraping.ipynb		00.intro_scraping.ipynb
01.pre-processing_references.ipynb		01.pre-processing_references.ipynb
02.eda.ipynb		02.eda.ipynb
03.eda.ipynb		03.eda.ipynb
04.eda.ipynb		04.eda.ipynb
05.model.ipynb		05.model.ipynb
06.model.ipynb		06.model.ipynb
07.model.ipynb		07.model.ipynb
08.model.ipynb		08.model.ipynb
09.model.ipynb		09.model.ipynb
10.model.ipynb		10.model.ipynb
3D_Plot.html		3D_Plot.html
README.md		README.md
env_requirements.txt		env_requirements.txt
hyperband.txt		hyperband.txt
required_python_version		required_python_version
requirements.txt		requirements.txt