Sentiment Analysis with LangChain and LLM

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.

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}")