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nbro
nbro
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Maintain How to keep track of the subject throughout sentences/entity in a sentence?

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. Say

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."

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 In the example above, in the 2nd sentence 'it' refers to StackExchange'StackExchange'. II 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. HoweverHowever, it doesn't sound like a very safe or accurate way to find the subject.

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

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

Maintain track of subject throughout sentences

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?

How to keep track of the subject/entity in a sentence?

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?

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johnny 5
johnny 5
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Maintain track of subject throughout sentences

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?