public/subjects/ai/nlp-scraper/audit/README.md

NLP-enriched News Intelligence platform

Preliminary

project
│   README.md
│   environment.yml
│
└───data
│   │   topic_classification_data.csv
│
└───results
│   │   topic_classifier.pkl
│   │   learning_curves.png
│   │   enhanced_news.csv
|
|───nlp_engine
│

Does the structure of the project is as below ?

Does the readme file give an introduction of the project, show the username, describe the feature engineering and show the best score the on the leaderboard ?

Does the environment contain all libraries used and their versions that are necessary to run the code ?

Scrapper

There are at least 300 news articles stored in the file system or the database.

Run the scrapper with python scrapper_news.py and fetch 3 documents. The scrapper is not expected to fetch 3 documents and stop by itself, you can stop it manually. It runs without any error and stores the 3 files as expected.

Topic classifier

Are the learning curves provided ?

Do the learning curves prove the topics classifier is trained without correctly - without overfitting ?

Can you run the topic classfier model on the test set without any error ?

Does the topic classifier score an accuracy higher than 95% ?

Scandal detection

Does the README.md explain the choice of embeddings and distance ?

Does the DataFrame flag the top 10 articles with the highest likelihood of environmental scandal ?

Is the distance or similarity saved in the DataFrame ?

NLP engine output on 300 articles

Does the DataFrame contain 300 different rows ?

Does the columns of the DataFrame are as expected ?

Date scrapped (date)
Title (str)
URL (str)
Body (str)
Org (str)
Topics (list str)
Sentiment (list float or float)
Scandal_distance (float)
Top_10 (bool)

Analyse the DataFrame with 300 articles: relevance of the topics matched, relevance of the sentiment, relevance of the scandal detected and relevance of the companies matched. The algorithms are not 100% accurate so you should expect a few issues in the results.

NLP engine on 3 articles

Can you run python nlp_enriched_news.py without any error ?

Does the output of the nlp engine correspond to the output below?

python nlp_enriched_news.py

Enriching <URL>:

Cleaning document ... (optional)

---------- Detect entities ----------

Detected <X> companies which are <company_1> and <company_2>

---------- Topic detection ----------

Text preprocessing ...

The topic of the article is: <topic>

---------- Sentiment analysis ----------

Text preprocessing ... (optional)
The title which is <title> is <sentiment>
The body of the article is <sentiment>

---------- Scandal detection ----------

Computing embeddings and distance ...

Environmental scandal detected for <entity>

Analyse the output: relevance of the topic(s) matched, relevance of the sentiment, relevance of the scandal detected (if detected on the three articles) and relevance of the companie(s) matched.