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
1. Install Required Libraries:
```bash
pip install langchain openai transformers
```
2. Set Up OpenAI API:
```python
import openai
openai.api_key = 'your_openai_api_key'
```
3. LangChain for Sentiment Analysis:
```python
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
```python
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)
```python
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
```python
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
```python
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](https://langchain.com/docs) and the [OpenAI API documentation](https://beta.openai.com/docs/).
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