Natural Language Processing Complete Guide: From Text Analysis to ChatGPT

😊 Sentiment Analysis

Determine emotional tone

• Social media monitoring
• Customer reviews analysis
• Brand reputation tracking

🌍 Translation

Convert between languages

• Google Translate
• Document translation
• Real-time interpretation

💬 Text Generation

Create human-like text

• ChatGPT and AI assistants
• Content creation
• Code generation

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
import re

class SentimentAnalyzer:
    def __init__(self):
        self.vectorizer = TfidfVectorizer(max_features=10000, stop_words='english')
        self.classifier = LogisticRegression(random_state=42)
        self.lemmatizer = WordNetLemmatizer()
        
        # Download required NLTK data
        nltk.download('punkt', quiet=True)
        nltk.download('stopwords', quiet=True)
        nltk.download('wordnet', quiet=True)
    
    def preprocess_text(self, text):
        """Clean and preprocess text"""
        # Convert to lowercase
        text = text.lower()
        
        # Remove special characters and digits
        text = re.sub(r'[^a-zA-Zs]', '', text)
        
        # Tokenize
        tokens = word_tokenize(text)
        
        # Remove stopwords and lemmatize
        stop_words = set(stopwords.words('english'))
        tokens = [self.lemmatizer.lemmatize(token) 
                 for token in tokens if token not in stop_words]
        
        return ' '.join(tokens)
    
    def prepare_data(self, texts, labels):
        """Preprocess texts and prepare for training"""
        processed_texts = [self.preprocess_text(text) for text in texts]
        return processed_texts, labels
    
    def train(self, texts, labels):
        """Train the sentiment analysis model"""
        # Preprocess data
        processed_texts, labels = self.prepare_data(texts, labels)
        
        # Split data
        X_train, X_test, y_train, y_test = train_test_split(
            processed_texts, labels, test_size=0.2, random_state=42
        )
        
        # Vectorize text
        X_train_vec = self.vectorizer.fit_transform(X_train)
        X_test_vec = self.vectorizer.transform(X_test)
        
        # Train classifier
        self.classifier.fit(X_train_vec, y_train)
        
        # Evaluate
        y_pred = self.classifier.predict(X_test_vec)
        accuracy = accuracy_score(y_test, y_pred)
        
        print(f"Model Accuracy: {accuracy:.4f}")
        print("
Classification Report:")
        print(classification_report(y_test, y_pred))
        
        return accuracy
    
    def predict(self, text):
        """Predict sentiment for a single text"""
        processed_text = self.preprocess_text(text)
        text_vec = self.vectorizer.transform([processed_text])
        prediction = self.classifier.predict(text_vec)[0]
        probability = self.classifier.predict_proba(text_vec)[0]
        
        return {
            'sentiment': prediction,
            'confidence': max(probability),
            'probabilities': {
                'negative': probability[0],
                'positive': probability[1]
            }
        }
    
    def predict_batch(self, texts):
        """Predict sentiment for multiple texts"""
        results = []
        for text in texts:
            result = self.predict(text)
            results.append(result)
        return results

# Usage example
# analyzer = SentimentAnalyzer()

# Sample data (in practice, you'd load from a dataset)
# sample_texts = [
#     "I love this product! It's amazing!",
#     "This is terrible, worst purchase ever.",
#     "It's okay, nothing special but not bad either."
# ]
# sample_labels = [1, 0, 1]  # 1 = positive, 0 = negative

# Train the model
# analyzer.train(sample_texts, sample_labels)

# Make predictions
# result = analyzer.predict("This movie is fantastic!")
# print(f"Prediction: {result}")

Q: Do I need to understand linguistics to work in NLP?

A: While helpful, it's not essential. Focus on understanding text processing techniques, machine learning fundamentals, and practical implementation. Many successful NLP engineers come from computer science backgrounds.

Q: What's the difference between NLP and computational linguistics?

A: NLP focuses on practical applications and getting computers to process language effectively. Computational linguistics is more academic, studying language structure and theoretical models of how language works.

Q: How do I handle multiple languages in NLP projects?

A: Use multilingual models like mBERT or XLM-R, ensure proper text encoding (UTF-8), consider language-specific preprocessing, and be aware of cultural context differences.

Q: What career opportunities exist in NLP?

A: Many opportunities in conversational AI, search engines, content moderation, translation services, and research. Roles include NLP Engineer, Research Scientist, and AI Product Manager with salaries ranging from $95,000 to $250,000+.