def basic_sentiment_analyzer(text):
    positive_words = {'good', 'great', 'excellent', 'happy', 'positive'}
    negative_words = {'bad', 'poor', 'terrible', 'unhappy', 'negative'}
    
    words = text.lower().split()
    positive_count = sum(1 for word in words if word in positive_words)
    negative_count = sum(1 for word in words if word in negative_words)
    
    if positive_count > negative_count:
        return 'positive'
    elif negative_count > positive_count:
        return 'negative'
    return 'neutral'

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression

def train_sentiment_classifier(texts, labels):
    vectorizer = TfidfVectorizer(max_features=1000)
    X = vectorizer.fit_transform(texts)
    
    classifier = LogisticRegression()
    classifier.fit(X, labels)
    
    return vectorizer, classifier

from transformers import pipeline

# Using pre-trained transformer model
sentiment_analyzer = pipeline("sentiment-analysis")

def analyze_sentiment(text):
    result = sentiment_analyzer(text)[0]
    return {
        'label': result['label'],
        'score': result['score']
    }

def aspect_based_sentiment(text, aspects):
    sentiments = {}
    doc = nlp(text)
    
    for aspect in aspects:
        relevant_sentences = [sent.text for sent in doc.sents 
                            if aspect in sent.text.lower()]
        if relevant_sentences:
            sentiment = analyze_sentiment(" ".join(relevant_sentences))
            sentiments[aspect] = sentiment
            
    return sentiments

import re
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer

def preprocess_text(text):
    # Convert to lowercase
    text = text.lower()
    
    # Remove special characters
    text = re.sub(r'[^a-zA-Z\s]', '', text)
    
    # Tokenization
    tokens = word_tokenize(text)
    
    # Remove stopwords
    stop_words = set(stopwords.words('english'))
    tokens = [token for token in tokens if token not in stop_words]
    
    # Lemmatization
    lemmatizer = WordNetLemmatizer()
    tokens = [lemmatizer.lemmatize(token) for token in tokens]
    
    return ' '.join(tokens)

from textblob import TextBlob

def extract_features(text):
    blob = TextBlob(text)
    return {
        'polarity': blob.sentiment.polarity,
        'subjectivity': blob.sentiment.subjectivity,
        'word_count': len(blob.words),
        'sentence_count': len(blob.sentences)
    }

from sklearn.metrics import classification_report, confusion_matrix
import seaborn as sns
import matplotlib.pyplot as plt

def evaluate_model(y_true, y_pred):
    # Classification report
    print(classification_report(y_true, y_pred))
    
    # Confusion matrix visualization
    cm = confusion_matrix(y_true, y_pred)
    sns.heatmap(cm, annot=True, fmt='d')
    plt.show()

from sklearn.model_selection import cross_val_score

def cross_validate_model(model, X, y, cv=5):
    scores = cross_val_score(model, X, y, cv=cv)
    print(f"Cross-validation scores: {scores}")
    print(f"Mean CV score: {scores.mean():.3f} (+/- {scores.std() * 2:.3f})")

def analyze_social_media_sentiment(posts):
    results = []
    for post in posts:
        sentiment = analyze_sentiment(post['text'])
        results.append({
            'post_id': post['id'],
            'sentiment': sentiment['label'],
            'confidence': sentiment['score'],
            'timestamp': post['timestamp']
        })
    return results

def analyze_customer_feedback(reviews):
    analysis = {
        'positive': 0,
        'negative': 0,
        'neutral': 0,
        'aspects': defaultdict(list)
    }
    
    for review in reviews:
        sentiment = analyze_sentiment(review['text'])
        analysis[sentiment['label']] += 1
        
        # Aspect-based analysis
        aspects = extract_aspects(review['text'])
        for aspect, sentiment in aspects.items():
            analysis['aspects'][aspect].append(sentiment)
    
    return analysis

from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer

def create_multilingual_analyzer():
    model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    
    return pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)

class SentimentStream:
    def __init__(self):
        self.analyzer = pipeline("sentiment-analysis")
        self.buffer = []
        
    def process_stream(self, text):
        sentiment = self.analyzer(text)[0]
        self.buffer.append({
            'text': text,
            'sentiment': sentiment['label'],
            'timestamp': datetime.now()
        })