aakinlalu/Food-Review-Classification

Train a model to class food review sentiments

FOOD REVIEW CLASSIFICATION CLI APPLICATION

Build classification model to classifier food review sentiments using logistic regression, naive bayes through sklearn and mlflow.

   |--food_review
   |      |--data/
   |      |
   |      |--image/
   |      |
   |      |--mlruns
   |      |
   |      |--metrics.py
   |      |
   |      |--pipeline.pkl
   |      |
   |      |--predict.py
   |      |
   |      |--sentiment_cli.py
   |      |
   |      |--train.py
   |
   |--README.md
   |
   |--requirment.txt
   |
   |--setup.py

What does the application do?

The Review Sentiment is a supervised machine learning cli application that identifies if a review has postive (1) or negative (0) sentiment with probability score.

How to install it

From the project directory in terminal:

1. create python enviroment using conda or venv
2. pip install -r requirement.txt

Example Dev Usage

1. from RUBIX directory cd food_review

2. Try to use help undertand the cli
   python sentiment_cli.py --help

   Usage: sentiment_cli.py [OPTIONS] COMMAND [ARGS]...

   Options: --help Show this message and exit.

   Commands:
   metrics
   metricsvisualizer
   predict

3. To use it to predict:

   1. Try with predict help:

      python sentiment_cli.py predict --help

      Usage: sentiment_cli.py predict [OPTIONS]

      Options:

      --text TEXT it can be single text review or text file with review with .txt/.csv --help Show this message and exit.

   2. Try to use predict "I do not like rice anymore":

      python sentiment_cli.py predict --text="I do not like rice anymore"

      Verbatism: I do not like rice anymore Sentiment Value: 0 Sentiment score: 0.42%

4. To check the metrics of the model:

   1. Check out help:
      python sentiment_cli.py metrics --help

      Usage: sentiment_cli.py metrics [OPTIONS]

      Options: --metric [all|accuracy|auc_score|f1_score] Select which metrics you want to see --help Show this message and exit.

   2. Check all the metrics:

      python sentiment_cli.py metrics --metric=all

      {'auc_score': 0.76, 'Accuracy': 0.76, 'f1_score': 0.75}

Build it

from Food-Review-Classification directory:
python setup.py develop