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The importance of domain adaption in sentiment analysis Domain
Encode General Domain

Financial Sentiment Analysis

financial analysis

Given a piece of text, output either a positive, negative, or neutral sentiment value.

Large Language Models(LLM) like BERT and GPT are designed to help computers understand the meaning of ambiguous language in text by using surrounding text to establish context. However, most of the open sourced models are generic in nature and lack domain specificity. For instance, the BERT framework was pre-trained using text from Wikipedia and can be fine-tuned with various tasks including sentiment analysis.

Select One Option

Results

General Purpose Bert

negative

23.2%
Positive
34.5%
Neutral
42.3%
Negative
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The Bristol Port Company has sealed a one million pound contract with Cooper Specialised Handling to supply it with four 45-tonne, customised reach stackers from Konecranes
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positive

Selected sentence has a Positive sentiment.

negative

But general purpose BERT predicted Negative.

Results

General Purpose Bert

negative

26.8%
Positive
31.5%
Neutral
42.0%
Negative
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sold out $tza 45 $put (down $1), which were hedging my 45 $call. letting the calls ride solo now.
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positive

Selected sentence has a Positive sentiment.

negative

But general purpose BERT predicted Negative.

Results

General Purpose Bert

negative

15.8%
Positive
31.0%
Neutral
53.2%
Negative
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The deal is subject to approval by the Norwegian competition authorities
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positive

Selected sentence has a Positive sentiment.

negative

But general purpose BERT predicted Negative.

SambaNova GPT_Banking

Domain Specific Workflow

We propose SambaNova GPT_Banking, a purpose-built, domain-specific language model for the banking and financial services industry.

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Domain Adaptation Training Using Dataflow-As-A-Service™

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