Machine Learning: أساسيات NLP

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

pipe = Pipeline([
    ("tfidf", TfidfVectorizer(max_features=5000, ngram_range=(1, 2))),
    ("clf", LogisticRegression(max_iter=500)),
])
pipe.fit(X_train_reviews, y_train_sentiment)
print(f"Sentiment accuracy: {pipe.score(X_test, y_test):.3f}")

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

import nltk
nltk.download("punkt_tab", quiet=True)
nltk.download("stopwords", quiet=True)
nltk.download("wordnet", quiet=True)

def preprocess_text(text):
    # تحويل إلى أحرف صغيرة
    text = text.lower()
    # إزالة الأحرف الخاصة والأرقام
    text = re.sub(r"[^a-zA-Z\s]", "", text)
    # التجزئة
    tokens = word_tokenize(text)
    # إزالة الكلمات الشائعة
    stop_words = set(stopwords.words("english"))
    tokens = [t for t in tokens if t not in stop_words and len(t) > 2]
    # الجذر
    stemmer = PorterStemmer()
    tokens_stemmed = [stemmer.stem(t) for t in tokens]
    return " ".join(tokens_stemmed)

# اختبار
reviews = [
    "This product is absolutely amazing! Best purchase ever.",
    "Terrible quality, broke after 2 days. Very disappointed.",
    "Decent product for the price, but shipping was slow.",
]

for review in reviews:
    cleaned = preprocess_text(review)
    print(f"Original: {review}")
    print(f"Cleaned:  {cleaned}\n")

# Function defined successfully

from sklearn.feature_extraction.text import CountVectorizer

reviews = [
    "Great laptop fast performance",
    "Terrible screen quality",
    "Good price fast delivery",
]

vectorizer = CountVectorizer()
X_bow = vectorizer.fit_transform(reviews)
print(f"Vocabulary: {vectorizer.vocabulary_}")
print(f"BoW matrix shape: {X_bow.shape}")

# Executed successfully

from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
import numpy as np

reviews = [
    "great product fast delivery happy customer",
    "terrible quality broke disappointed waste money",
    "great quality decent price fast shipping",
    "awful product terrible experience never buy again",
    "excellent quality great value fast delivery",
]

labels = [1, 0, 1, 0, 1]  # 1=إيجابي، 0=سلبي

# الطريقة 1: Bag-of-Words (العدد)
bow = CountVectorizer()
X_bow = bow.fit_transform(reviews)
print(f"BoW vocabulary size: {len(bow.vocabulary_)}")

# الطريقة 2: TF-IDF
tfidf = TfidfVectorizer(max_features=1000)
X_tfidf = tfidf.fit_transform(reviews)
print(f"TF-IDF shape: {X_tfidf.shape}")

# الطريقة 3: N-gram (التقاط العبارات)
tfidf_ngram = TfidfVectorizer(ngram_range=(1, 2), max_features=1000)
X_ngram = tfidf_ngram.fit_transform(reviews)
print(f"TF-IDF with bigrams shape: {X_ngram.shape}")
print(f"Top bigrams: {[w for w in tfidf_ngram.get_feature_names_out() if ' ' in w][:10]}")

# Executed successfully

from gensim.models import Word2Vec
from gensim.test.utils import common_texts
import numpy as np

# تدريب Word2Vec على عينة من الجسم
# عمليًا، استخدم جسم بيانات المراجعة الحقيقي
sentences = [
    ["great", "product", "fast", "delivery", "happy"],
    ["terrible", "quality", "broke", "disappointed"],
    ["great", "quality", "decent", "price"],
    ["awful", "product", "terrible", "experience"],
    ["excellent", "quality", "great", "value"],
    ["fast", "shipping", "good", "price", "recommend"],
    ["poor", "quality", "slow", "delivery", "angry"],
    ["amazing", "product", "best", "purchase", "happy"],
]

model = Word2Vec(sentences, vector_size=50, window=3, min_count=1, workers=4, epochs=50)

# تشابه الكلمات
print("Similar to 'great':")
for word, sim in model.wv.most_similar("great", topn=5):
    print(f"  {word}: {sim:.3f}")

# متجه الكلمة
print(f"\nVector for 'quality': {model.wv['quality'][:5]}... (50-dim)")

Similar to 

from sklearn.decomposition import PCA
import matplotlib.pyplot as plt

words = ["great", "terrible", "happy", "angry", "fast", "slow",
         "quality", "price", "product", "delivery"]

vectors = [model.wv[w] for w in words]
vectors_2d = PCA(n_components=2).fit_transform(vectors)

fig, ax = plt.subplots(figsize=(10, 8))
ax.scatter(vectors_2d[:, 0], vectors_2d[:, 1], s=100)
for i, word in enumerate(words):
    ax.annotate(word, (vectors_2d[i, 0], vectors_2d[i, 1]), fontsize=12)
ax.set_title("Word2Vec Embedding Visualization")
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig("word2vec_vis.png", dpi=150)

# Executed successfully

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.metrics import classification_report, confusion_matrix
import numpy as np

# محاكاة مراجعات المنتجات
rng = np.random.default_rng(42)

positive_templates = [
    "great product love it highly recommend",
    "excellent quality fast delivery satisfied",
    "amazing value best purchase ever happy",
    "good product decent price works well",
    "love this item perfect condition great",
]

negative_templates = [
    "terrible quality broke after one day",
    "awful product waste money very disappointed",
    "poor quality slow delivery never again",
    "worst purchase defective product angry",
    "bad product cheap material not recommend",
]

# توليد 2000 مراجعة
reviews = []
labels = []
for _ in range(1000):
    reviews.append(rng.choice(positive_templates))
    labels.append(1)
    reviews.append(rng.choice(negative_templates))
    labels.append(0)

X_train, X_test, y_train, y_test = train_test_split(reviews, labels, test_size=0.2, random_state=42)

# خط أنابيب TF-IDF + الانحدار اللوجستي
pipe = Pipeline([
    ("tfidf", TfidfVectorizer(max_features=5000, ngram_range=(1, 2), min_df=2)),
    ("clf", LogisticRegression(max_iter=500, C=1.0)),
])

pipe.fit(X_train, y_train)
y_pred = pipe.predict(X_test)

print(f"Accuracy: {pipe.score(X_test, y_test):.4f}")
print(f"\nClassification Report:\n{classification_report(y_test, y_pred, target_names=['Negative', 'Positive'])}")

# استخراج الكلمات الأكثر أهمية لكل عاطفة
feature_names = pipe.named_steps["tfidf"].get_feature_names_out()
coefs = pipe.named_steps["clf"].coef_[0]
top_positive = np.argsort(coefs)[-10:][::-1]
top_negative = np.argsort(coefs)[:10]

print("\nTop positive words:")
for idx in top_positive:
    print(f"  {feature_names[idx]:20s}: {coefs[idx]:.3f}")

print("\nTop negative words:")
for idx in top_negative:
    print(f"  {feature_names[idx]:20s}: {coefs[idx]:.3f}")

Top positive words:
Top negative words: