Advanced Natural Language Processing with LLMs

Advanced Natural Language Processing with LLMs

Introduction

Language models can be used to perform advanced natural language processing (NLP) tasks. This tutorial explores the different ways to implement these capabilities with LLMs.

Types of NLP Tasks

1. Text Analysis

• Sentiment analysis
• Tone analysis
• Style analysis
• Coherence analysis
• Structure analysis

2. Classification

• Document classification
• Sentiment classification
• Topic classification
• Language classification
• Genre classification

3. Information Extraction

• Entity extraction
• Relation extraction
• Keyword extraction
• Summary extraction
• Quote extraction

Implementation

1. Text Analyzer

from openai import OpenAI
import json
from typing import List, Dict, Any
import numpy as np

class TextAnalyzer:
    def __init__(self, api_key: str):
        self.client = OpenAI(api_key=api_key)
    
    def analyze_text(self, text: str, analysis_type: str = "sentiment") -> Dict[str, Any]:
        prompt = self._create_analysis_prompt(text, analysis_type)
        
        response = self.client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": "Tu es un expert en analyse de texte."},
                {"role": "user", "content": prompt}
            ],
            temperature=0.3
        )
        
        return json.loads(response.choices[0].message.content)
    
    def _create_analysis_prompt(self, text: str, analysis_type: str) -> str:
        return f"""
        Analyse le texte suivant de type {analysis_type} :
        
        Texte : {text}
        
        Fournis :
        1. L'analyse détaillée
        2. Les points clés
        3. Les insights
        4. Les recommandations
        
        Réponds au format JSON.
        """