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https://github.com/avivace/reviews-sentiment
Data analytics, exploration, sentiment analysis and topic analysis (LDA) on Amazon customer reviews. And cool interactive plots.
https://github.com/avivace/reviews-sentiment
amazon-reviews analytics customer-data data-analytics data-visualization data-visualization-project lda plots sentiment-analysis topic-analysis user-review
Last synced: 2 months ago
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Data analytics, exploration, sentiment analysis and topic analysis (LDA) on Amazon customer reviews. And cool interactive plots.
- Host: GitHub
- URL: https://github.com/avivace/reviews-sentiment
- Owner: avivace
- Created: 2019-11-21T22:11:11.000Z (about 5 years ago)
- Default Branch: develop
- Last Pushed: 2022-07-30T12:39:00.000Z (over 2 years ago)
- Last Synced: 2024-10-04T12:45:06.745Z (3 months ago)
- Topics: amazon-reviews, analytics, customer-data, data-analytics, data-visualization, data-visualization-project, lda, plots, sentiment-analysis, topic-analysis, user-review
- Language: Jupyter Notebook
- Homepage: https://avivace.github.io/reviews-sentiment
- Size: 11.3 MB
- Stars: 29
- Watchers: 3
- Forks: 10
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Analytics on Amazon Reviews
Data Analytics exam final project, [MSc in Computer Science](https://github.com/avivace/compsci).
By [Matteo Coppola](https://github.com/matteocoppola), [Luca Palazzi](https://github.com/lucapalazzi), [Antonio Vivace](https://github.com/avivace).
> Exploration, Sentiment Analysis, Topic Analysis (LDA) and a VueJS web application exposing the trained models.
[GO. PLAY. WITH THE PLOTS.](https://avivace.github.io/reviews-sentiment) (web demo deployment)
[Documentation](report.pdf)
#### Exploration
#### Web demo
## Run
Set up the a Python virtual environment and install required packages
```bash
cd scripts
python3 -m venv .
source bin/activate
pip3 install -r requirements.txt
python3 -m spacy download en
```
Optionally, install a ipynb kernel to use the venv packages
```bash
pip3 install --user ipykernel
python -m ipykernel install --user --name=myenv
# Check the installed kernels
jupyter kernelspec list
# Run Jupyter
jupyter lab
```
Now, to run the full pipeline:
```bash
python3 main.py
```
A Flask application exposes a simple API (on port 5000) allowing the trained models to be used on demand via simple HTTP requests (in main.py). The VueJS application needs a recent version of NodeJS and npm.
```bash
cd webapp
npm install
# serve the web application with hot reload at localhost:8080/reviews-sentiment
npm run serve
# builds the web application for production
npm run build
# deploys the build on the master branch, making github serve it on https://avivace.github.io/reviews-sentiment
npm run deploy
```
#### Antuz notes
Accent is `#B71C1C`, typeface is *Barlow* 500. On the plots and graphs, typeface is *Inter* 600, palette is `#4DAF4A`, `#FF7F00`, `#C73E31`.
#### Final notes from our supervisor, E.Fersini
Unverified/Spam "boom" happens around the first-publishing of some product, aggregating data from a category will hardly show this (there are papers on this)