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 look like the above?

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

Scraper

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

Run the scraper with python scraper_news.py and fetch 3 documents. The scraper is not expected to fetch 3 documents and stop by itself, you can stop it manually.

Does it run without any error and store the 3 files as expected?

Topic classifier

Are the learning curves provided?

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

Can you run the topic classifier 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?

Are the columns of the DataFrame as expected?

Date scraped (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 company(ies) matched.