I'm working on Sentiment Analysis, using HuggingFace to perform sentiment analysis on articles

 classifier = pipeline('sentiment-analysis', model="nlptown/bert-base-multilingual-uncased-sentiment")
 classifier(['We are very happy to show you the 🤗 Transformers library.',  "We hope you don't hate it."])

This returns

label: POSITIVE, with score: 0.9998

label: NEGATIVE, with score: 0.5309

Now I'm trying to understand how to keep track of a subject when performing the sentiment analysis.

Suppose I'm given a sentence like this.

StackExchange is a great website. It helps users answer questions. Hopefully, someone will help answer this question.

I would like to keep track of the subject when performing sentiment analysis. In the example above, in the 2nd sentence 'it' refers to 'StackExchange'. I would like to be able to do track a subject between sentences.

Now, I could try to manually try to parse this by finding the verb and trying to figure find the phrase that comes before it. However, it doesn't sound like a very safe or accurate way to find the subject.

Alternatively, I could train similar to a Named Entity Recognition. However, finding a dataset for this is very hard, and training it would be very time-consuming.

How can I keep track of an entity within an article?