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

# NLP-enriched News Intelligence platform

The goal of this project is to build an NLP-enriched News Intelligence
platform. News analysis is a trending and important topic. The analysts get
their information from the news and the amount of available information is
limitless. Having a platform that helps to detect the relevant information is
definitely valuable.

The platform connects to a news data source, detects the entities, detects the
topic of the article, analyse the sentiment and ...

### Scraper

News data source:

- Find a news website that is easy to scrape. I could have chosen the website,
  but the news' websites change their scraping policy frequently.
- Store the following information either in one file per day or in a SQL
  database:
  - unique ID,
  - URL,
  - date,
  - headline,
  - body of the article.

Use data from the last week otherwise the volume may be too high.

There should be at least 300 articles stored in your file system or SQL
database.

### NLP engine

In production architectures, the NLP engine delivers a live output based on the
news that are delivered in a live stream data by the scraper. However, it
required advanced Python skills that is not a requisite for the AI branch.
To simplify this step the scraper and the NLP engine are used independently in
the project. The scraper fetches the news and store them in the data structure
(either the file system or the SQL database) and then, the NLP engine runs on
the stored data.

Here how the NLP engine should process the news:

### **1. Entities detection:**

The goal is to detect all the entities in the document (headline and body). The
type of entity we focus on is `ORG`. This corresponds to companies and
organizations. This information should be stored.

- Detect all companies using `SpaCy NER` on the body of the text.

https://towardsdatascience.com/named-entity-recognition-with-nltk-and-spacy-8c4a7d88e7da

### **2. Topic detection:**

The goal is to detect what the article is dealing with: Tech, Sport, Business,
Entertainment or Politics. To do so, a labelled dataset is provided: [training
data](bbc_news_train.csv) and [test data](bbc_news_test.csv). From this
dataset, build a classifier that learns to detect the right topic in the
article. The trained model should be stored as `topic_classifier.pkl`. Make
sure the model can be used easily (with the preprocessing pipeline built for
instance) because the audit requires the auditor to test the model.

Save the plot of learning curves (`learning_curves.png`) in `results` to prove
that the model is trained correctly and not overfitted.

- Learning constraints: **Score on test: > 95%**

- **Optional**: If you want to train a news' topic classifier based on a more
  challenging dataset, you can use the
  [following](https://www.kaggle.com/rmisra/news-category-dataset) which is
  based on 200k news headlines.

### **3. Sentiment analysis:**

The goal is to detect the sentiment (positive, negative or neutral) of the news
articles. To do so, use a pre-trained sentiment model. I suggest to use:
`NLTK`. There are 3 reasons for which we use a pre-trained model:

1. As a Data Scientist, you should learn to use a pre-trained model. There are
   so many models available and trained that sometimes you don't need to train
   one from scratch.
2. Labelled news data for sentiment analysis are very expensive. Companies as
   [SESAMm](https://www.sesamm.com/) provide this kind of services.

- [Sentiment analysis](https://en.wikipedia.org/wiki/Sentiment_analysis)

### **4. Scandal detection **

The goal is to detect environmental disaster for the detected companies. Here
is the methodology that should be used:

- Define keywords that correspond to environmental disaster that may be caused
  by companies: pollution, deforestation etc ... Here is an example of disaster
  we want to detect: https://en.wikipedia.org/wiki/MV_Erika. Pay attention to
  not use ambiguous words that make sense in the context of an environmental
  disaster but also in another context. This would lead to detect a false
  positive natural disaster.

- Compute the embeddings of the keywords.

- Compute the distance between the embeddings of the keywords and all sentences
  that contain an entity. Explain in the `README.md` the embeddings chosen and
  why. Similarly explain the distance or similarity chosen and why.

- Save the distance

- Flag the top 10 articles.

### 5. **Source analysis (optional)**

The goal is to show insights about the news' source you scraped.
This requires to scrap data on at least 5 days (a week ideally). Save the plots
in the `results` folder.

Here are examples of insights:

- Per day:

  - Proportion of topics per day
  - Number of articles
  - Number of companies mentioned
  - Sentiment per day

- Per companies:

  - Companies mentioned the most
  - Sentiment per companies

### Deliverables

The structure of the project is:

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

```

1.  Run the scraper until it fetches at least 300 articles

```
python scraper_news.py

1. scraping <URL>
        requesting ...
        parsing ...
        saved in <path>

2. scraping <URL>
        requesting ...
        parsing ...
        saved in <path>

```

2. Run on these 300 articles the NLP engine.

Save a `DataFrame`:

Date scraped (date)
Title (`str`)
URL (`str`)
Body (`str`)
Org (`str`)
Topics (`list str`)
Sentiment (`list float1 or `float`)
Scandal_distance (`float`)
Top_10 (`bool`)

```prompt
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>
```

I strongly suggest creating a data structure (dictionary for example) to save all the intermediate result. Then, a boolean argument `cache` fetched the intermediate results when they are already computed.

Resources:

- https://www.youtube.com/watch?v=XVv6mJpFOb0