wehub-resource-sync
99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
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""" Sentiment Analysis example - shows how to use the slim-sentiment-tool. In this example, we will:
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1. Review several summary earnings transcripts, looking to evaluate the overall sentiment as
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'positive', 'negative', or 'neutral'
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2. Evaluate a single transcript, and apply if...then based on the result and confidence level.
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3. Run through a list of earnings transcripts with journaling activated to display the multi-step
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process on the screen.
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"""
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from llmware.agents import LLMfx
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earnings_transcripts = [
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"This is one of the best quarters we can remember for the industrial sector with significant growth across the "
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"board in new order volume, as well as price increases in excess of inflation. We continue to see very strong "
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"demand, especially in Asia and Europe. Accordingly, we remain bullish on the tier 1 suppliers and would be "
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"accumulating more stock on any dips. ",
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"Not the worst results, but overall we view as negative signals on the direction of the economy, and the likely "
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"short-term trajectory for the telecom sector, and especially larger market leaders, including AT&T, Comcast, and"
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"Deutsche Telekom.",
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"This quarter was a disaster for Tesla, with falling order volume, increased costs and supply, and negative "
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"guidance for future growth forecasts in 2024 and beyond.",
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"On balance, this was an average result, with earnings in line with expectations and no big surprises to either "
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"the positive or the negative."
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]
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def get_one_sentiment_classification(text):
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"""This example shows a basic use to get a sentiment classification and use the output programmatically. """
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# simple basic use to get the sentiment on a single piece of text
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agent = LLMfx(verbose=True)
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agent.load_tool("sentiment")
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sentiment = agent.sentiment(text)
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# look at the output
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print("sentiment: ", sentiment)
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for keys, values in sentiment.items():
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print(f"{keys}-{values}")
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# two key attributes of the sentiment output dictionary
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sentiment_value = sentiment["llm_response"]["sentiment"]
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confidence_level = sentiment["confidence_score"]
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# use the sentiment classification as a 'if...then' decision point in a process
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if "positive" in sentiment_value:
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print("sentiment is positive .... will take 'positive' analysis path ...", sentiment_value)
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if "positive" in sentiment_value and confidence_level > 0.8:
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print("sentiment is positive with high confidence ... ", sentiment_value, confidence_level)
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return sentiment
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def review_batch_earning_transcripts():
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""" This example highlights how to review multiple earnings transcripts and iterate through a batch
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using the load_work mechanism. """
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agent = LLMfx()
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agent.load_tool("sentiment")
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# iterating through a larger list of samples
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# note: load_work method is a flexible input mechanism - pass a string, list, dictionary or combination, and
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# it will 'package' as iterable units of processing work for the agent
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agent.load_work(earnings_transcripts)
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while True:
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output = agent.sentiment()
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# print("update: test - output - ", output)
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if not agent.increment_work_iteration():
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break
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response_output = agent.response_list
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agent.clear_work()
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agent.clear_state()
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return response_output
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if __name__ == "__main__":
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# first - quick illustration of getting a sentiment classification
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# and using in an "if...then"
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sentiment = get_one_sentiment_classification(earnings_transcripts[0])
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# second - iterate thru a batch of transcripts and apply a sentiment classification
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# response_output = review_batch_earning_transcripts()
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