Here's a quick guide on how to perform sentiment analysis and other tasks using LangChain, LLM (Large Language Models), NLP (Natural Language Processing), and statistical analytics.
Sentiment Analysis with LangChain and LLM
- Install Required Libraries:
pip install langchain openai transformers
- Set Up OpenAI API:
import openai
openai.api_key = 'your_openai_api_key'
- LangChain for Sentiment Analysis:
from langchain.llms import OpenAI
from langchain import Chain
# Initialize OpenAI LLM
llm = OpenAI(model="text-davinci-003")
# Define a function for sentiment analysis
def analyze_sentiment(text):
response = llm.completion(
prompt=f"Analyze the sentiment of the following text: {text}",
max_tokens=60
)
return response.choices[0].text.strip()
# Example usage
text = "I love the new design of the website!"
sentiment = analyze_sentiment(text)
print(f"Sentiment: {sentiment}")
Additional NLP Tasks with LangChain and LLM
Text Summarization
def summarize_text(text):
response = llm.completion(
prompt=f"Summarize the following text: {text}",
max_tokens=150
)
return response.choices[0].text.strip()
# Example usage
text = "Your detailed article or document here."
summary = summarize_text(text)
print(f"Summary: {summary}")
Named Entity Recognition (NER)
def extract_entities(text):
response = llm.completion(
prompt=f"Extract the named entities from the following text: {text}",
max_tokens=100
)
return response.choices[0].text.strip()
# Example usage
text = "OpenAI, founded in San Francisco, is a leading AI research institute."
entities = extract_entities(text)
print(f"Entities: {entities}")
Statistical Analytics with NLP
Word Frequency Analysis
from collections import Counter
import re
def word_frequency_analysis(text):
words = re.findall(r'\w+', text.lower())
frequency = Counter(words)
return frequency
# Example usage
text = "This is a sample text with several words. This text is for testing."
frequency = word_frequency_analysis(text)
print(f"Word Frequency: {frequency}")
Sentiment Score Aggregation
def sentiment_score(text):
sentiment = analyze_sentiment(text)
if "positive" in sentiment.lower():
return 1
elif "negative" in sentiment.lower():
return -1
else:
return 0
# Example usage
texts = ["I love this!", "This is bad.", "It's okay."]
scores = [sentiment_score(t) for t in texts]
average_score = sum(scores) / len(scores)
print(f"Average Sentiment Score: {average_score}")
For more advanced uses and customization, refer to the LangChain documentation and the OpenAI API documentation.
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