I'm working on Sentiment Analysis, using HuggingFace 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. Say 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. e.g 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 doesn't sound like a very safe or accurate way to find the subject.

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

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