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.