diff --git a/subjects/ai/nlp-scraper/README.md b/subjects/ai/nlp-scraper/README.md index 4b8b6dfb3..3be89edd9 100644 --- a/subjects/ai/nlp-scraper/README.md +++ b/subjects/ai/nlp-scraper/README.md @@ -1,4 +1,4 @@ -# NLP-enriched News Intelligence platform +## 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 @@ -7,7 +7,8 @@ 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 ... +topic of the article, analyses the sentiment and performs a scandal detection +analysis. ### Scraper @@ -40,7 +41,7 @@ the stored data. Here how the NLP engine should process the news: -### **1. Entities detection:** +#### **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 @@ -51,7 +52,7 @@ organizations. This information should be stored. [Named Entity Recognition with NLTK and SpaCy](https://towardsdatascience.com/named-entity-recognition-with-nltk-and-spacy-8c4a7d88e7da) -### **2. Topic detection:** +#### **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 @@ -71,7 +72,7 @@ that the model is trained correctly and not overfitted. [following](https://www.kaggle.com/rmisra/news-category-dataset) which is based on 200k news headlines. -### **3. Sentiment analysis:** +#### **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: @@ -85,29 +86,32 @@ articles. To do so, use a pre-trained sentiment model. I suggest to use: - [Sentiment analysis](https://en.wikipedia.org/wiki/Sentiment_analysis) -### **4. Scandal detection ** +#### **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. + 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 [embeddings of the + keywords](https://en.wikipedia.org/wiki/Word_embedding#Software). -- 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. +- Compute the distance ([here some + examples](https://www.nltk.org/api/nltk.metrics.distance.html#module-nltk.metrics.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 +- Save a metric to unify all the distances calculated per article. - Flag the top 10 articles. -### 5. **Source analysis (optional)** +#### 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 @@ -129,24 +133,20 @@ Here are examples of insights: ### Deliverables -The structure of the project is: +The expected 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 -│ - +. +├── data +│   └── date_scrape_data.csv +├── nlp_enriched_news.py +├── README.md +├── results +│   ├── topic_classifier.pkl +│   ├── enhanced_news.csv +│   └── learning_curves.png +└── scraper_news.py ``` 1. Run the scraper until it fetches at least 300 articles @@ -166,52 +166,60 @@ python scraper_news.py ``` -2. Run on these 300 articles the NLP engine. +2. Run on these 300 articles the NLP engine. The script `nlp_eneriched_news.py` + should: -Save a `DataFrame`: + - Save a `DataFrame` with the following struct: -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`) + ``` + Unique ID (`uuid` or `int`) + URL (`str`) + Date scraped (`date`) + Headline (`str`) + Body (`str`) + Org (`list str`) + Topics (`list str`) + Sentiment (`list float` or `float`) + Scandal_distance (`float`) + Top_10 (`bool`) + ``` -```prompt -python nlp_enriched_news.py + - Have a similar output while it process the articles -Enriching : + ```prompt + python nlp_enriched_news.py -Cleaning document ... (optional) + Enriching : ----------- Detect entities ---------- + Cleaning document ... (optional) -Detected companies which are and + ---------- Detect entities ---------- ----------- Topic detection ---------- + Detected companies which are and -Text preprocessing ... + ---------- Topic detection ---------- -The topic of the article is: + Text preprocessing ... ----------- Sentiment analysis ---------- + The topic of the article is: -Text preprocessing ... (optional) -The title which is is <sentiment> -The body of the article is <sentiment> + ---------- Sentiment analysis ---------- ----------- Scandal detection ---------- + Text preprocessing ... (optional) + The article <title> has a <sentiment> sentiment -Computing embeddings and distance ... + ---------- Scandal detection ---------- -Environmental scandal detected for <entity> -``` + Computing embeddings and distance ... -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. + Environmental scandal detected for <entity> + ``` -Resources: +> 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. -- https://www.youtube.com/watch?v=XVv6mJpFOb0 +### Notions + +- [Web Scraping](https://www.youtube.com/watch?v=XVv6mJpFOb0) +- [Sentiment analysis](https://en.wikipedia.org/wiki/Sentiment_analysis) diff --git a/subjects/ai/nlp-scraper/audit/README.md b/subjects/ai/nlp-scraper/audit/README.md index 920a10d55..7609587b7 100644 --- a/subjects/ai/nlp-scraper/audit/README.md +++ b/subjects/ai/nlp-scraper/audit/README.md @@ -2,25 +2,7 @@ ##### 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 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? @@ -28,7 +10,7 @@ project ##### 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. +##### 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? @@ -54,20 +36,7 @@ project ###### 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) - -``` +###### 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. @@ -75,36 +44,6 @@ Top_10 (bool) ###### Can you run `python nlp_enriched_news.py` without any error? -###### Does the output of the NLP engine correspond to the output below? - -```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> -``` +###### 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.