#### NLP-enriched News Intelligence platform ##### Preliminary ###### Does the structure of the project look like the one described in the subject? ###### 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 defined in the subject `Deliverable` section? ##### 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 defined in the subject `Deliverable` section? ##### 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.