# Install necessary packages
!pip install tensorflow keras numpy pandas scikit-learn gensim requests tqdm nltk
 
# Download and install ISRI Stemmer (from NLTK)
import nltk
nltk.download('stopwords')
from nltk.stem.isri import ISRIStemmer

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[nltk_data] Error loading arabic_reshaper: Package 'arabic_reshaper'
[nltk_data]     not found in index
[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Unzipping corpora/stopwords.zip.

import os
import numpy as np
import pandas as pd
import tensorflow as tf
import gensim
import requests
import zipfile
from tqdm import tqdm
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout
from nltk.stem.isri import ISRIStemmer
from nltk.corpus import stopwords

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

import pandas as pd
 
# Define the path to your CSV file in Google Drive
csv_path = "/content/drive/My Drive/ANLP/SEC05/Arabic Sentiment Analysis Dataset - SS2030.csv"  # Update with the actual path
 
# Read the CSV file
df = pd.read_csv(csv_path, encoding="utf-8")  # Use 'utf-8-sig' if needed
df = df.dropna()  # Remove missing values
 
# Display sample
print(df.head())

                                                text  Sentiment
0            حقوق المرأة 💚💚💚 https://t.co/Mzf90Ta5g1          1
1  RT @___IHAVENOIDEA: حقوق المرأة في الإسلام. ht...          1
2  RT @saud_talep: Retweeted لجنة التنمية بشبرا (...          1
3  RT @MojKsa: حقوق المرأة التي تضمنها لها وزارة ...          1
4  RT @abm112211: ولي امر الزوجة او ولي الزوجة او...          1

# Initialize ISRI Stemmer
stemmer = ISRIStemmer()
stop_words = set(stopwords.words("arabic"))
 
# Function to preprocess Arabic text
def preprocess_arabic_text(text):
    text = str(text)  # Ensure it's a string
    text = text.replace("،", "").replace(".", "").replace("؟", "").replace("!", "")
    text = text.replace("\n", " ").replace("\r", " ")
    words = text.split()  # Tokenize manually (since ISRI doesn't tokenize)
    words = [stemmer.stem(word) for word in words if word not in stop_words]  # Stem and remove stopwords
    return " ".join(words)
 
# Apply preprocessing
df["processed_text"] = df["text"].apply(preprocess_arabic_text)
df.columns = df.columns.str.strip()
df = df.rename(columns={"Sentiment": "label"})
 
# Display processed data
df.head()

                                                text  label  \
0            حقوق المرأة 💚💚💚 https://t.co/Mzf90Ta5g1      1   
1  RT @___IHAVENOIDEA: حقوق المرأة في الإسلام. ht...      1   
2  RT @saud_talep: Retweeted لجنة التنمية بشبرا (...      1   
3  RT @MojKsa: حقوق المرأة التي تضمنها لها وزارة ...      1   
4  RT @abm112211: ولي امر الزوجة او ولي الزوجة او...      1   
 
                                      processed_text  
0                 حقق رأة 💚💚💚 https://tco/Mzf90Ta5g1  
1  RT @___IHAVENOIDEA: حقق رأة سلم https://tco/ps...  
2  RT @saud_talep: Retweeted لجن نمي شبر (@Shubra...  
3  RT @MojKsa: حقق رأة تضم وزر عدل https://tco/QU...  
4  RT @abm112211: ولي امر زوج او ولي زوج او ولي ر...

# Tokenization
tokenizer = Tokenizer(num_words=10000)  # Keep top 10K words
tokenizer.fit_on_texts(df["processed_text"])
 
# Convert text to sequences
X_sequences = tokenizer.texts_to_sequences(df["processed_text"])
X_padded = pad_sequences(X_sequences, maxlen=100)  # Standardize length
 
# Labels
y = np.array(df["label"])
 
# Split dataset
X_train, X_test, y_train, y_test = train_test_split(X_padded, y, test_size=0.2, random_state=42)

# Define Trainable Embedding Model
trainable_model = Sequential([
    Embedding(input_dim=10000, output_dim=300, input_length=100, trainable=True),
    LSTM(64, return_sequences=True),
    LSTM(32),
    Dense(1, activation='sigmoid')
])
 
# Compile the model
trainable_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
 
# Train the model
trainable_model.fit(X_train, y_train, epochs=5, batch_size=32, validation_data=(X_test, y_test))
 
# Evaluate
trainable_loss, trainable_acc = trainable_model.evaluate(X_test, y_test)
print(f"Trainable Embedding Model Accuracy: {trainable_acc:.2f}")

Epoch 1/5
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  warnings.warn(
[1m107/107[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m23s[0m 179ms/step - accuracy: 0.7188 - loss: 0.5467 - val_accuracy: 0.8566 - val_loss: 0.3537
Epoch 2/5
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Epoch 3/5
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Epoch 4/5
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Epoch 5/5
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[1m27/27[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 29ms/step - accuracy: 0.8257 - loss: 0.6966
Trainable Embedding Model Accuracy: 0.85

# Download AraVec Word2Vec Model
aravec_url = "https://bakrianoo.ewr1.vultrobjects.com/aravec/full_grams_cbow_300_twitter.zip"
aravec_zip_path = "aravec.zip"
aravec_model_path = "full_grams_cbow_300_twitter.mdl"
 
if not os.path.exists(aravec_model_path):
    response = requests.get(aravec_url, stream=True)
    with open(aravec_zip_path, "wb") as file:
        for chunk in tqdm(response.iter_content(chunk_size=1024)):
            file.write(chunk)
 
    # Extract the zip file
    with zipfile.ZipFile(aravec_zip_path, "r") as zip_ref:
        zip_ref.extractall("aravec")
 
# Load Word2Vec model
word2vec_model = gensim.models.Word2Vec.load('./aravec/full_grams_cbow_300_twitter.mdl')

3247588it [00:35, 91383.82it/s]

word2vec_model = gensim.models.Word2Vec.load('./aravec/full_grams_cbow_300_twitter.mdl')

embedding_dim = 300
embedding_matrix = np.zeros((len(tokenizer.word_index) + 1, embedding_dim))
 
for word, i in tokenizer.word_index.items():
    if word in word2vec_model.wv:  # Corrected: Use .wv to check vocabulary
        embedding_matrix[i] = word2vec_model.wv[word]  # Corrected: Use .wv to access word vectors
 
print(f"Embedding matrix shape: {embedding_matrix.shape}")

Embedding matrix shape: (11710, 300)

# Define LSTM Model with Pretrained Embeddings
pretrained_model = Sequential([
    Embedding(input_dim=len(tokenizer.word_index) + 1, output_dim=embedding_dim,
              weights=[embedding_matrix], input_length=100, trainable=False),  # Use pretrained embeddings (Frozen)
    LSTM(128, return_sequences=True),
    Dropout(0.3),
    LSTM(64),
    Dense(32, activation='relu'),
    Dense(1, activation='sigmoid')
])
 
# Compile model
pretrained_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
 
# Display model summary
pretrained_model.summary()

/usr/local/lib/python3.11/dist-packages/keras/src/layers/core/embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.
  warnings.warn(
[1mModel: "sequential_1"[0m
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃[1m [0m[1mLayer (type)                        [0m[1m [0m┃[1m [0m[1mOutput Shape               [0m[1m [0m┃[1m [0m[1m        Param #[0m[1m [0m┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ embedding_1 ([38;5;33mEmbedding[0m)              │ ?                           │       [38;5;34m3,513,000[0m │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_2 ([38;5;33mLSTM[0m)                        │ ?                           │     [38;5;34m0[0m (unbuilt) │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dropout ([38;5;33mDropout[0m)                    │ ?                           │               [38;5;34m0[0m │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ lstm_3 ([38;5;33mLSTM[0m)                        │ ?                           │     [38;5;34m0[0m (unbuilt) │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_1 ([38;5;33mDense[0m)                      │ ?                           │     [38;5;34m0[0m (unbuilt) │
├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤
│ dense_2 ([38;5;33mDense[0m)                      │ ?                           │     [38;5;34m0[0m (unbuilt) │
└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘
[1m Total params: [0m[38;5;34m3,513,000[0m (13.40 MB)
[1m Trainable params: [0m[38;5;34m0[0m (0.00 B)
[1m Non-trainable params: [0m[38;5;34m3,513,000[0m (13.40 MB)

# Train the model
pretrained_model.fit(X_train, y_train, epochs=5, batch_size=32, validation_data=(X_test, y_test))
 
# Evaluate model
pretrained_loss, pretrained_acc = pretrained_model.evaluate(X_test, y_test)
print(f"Pretrained Word2Vec Model Accuracy: {pretrained_acc:.2f}")

Epoch 1/5
[1m107/107[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m22s[0m 174ms/step - accuracy: 0.7081 - loss: 0.5336 - val_accuracy: 0.8120 - val_loss: 0.3891
Epoch 2/5
[1m107/107[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m18s[0m 171ms/step - accuracy: 0.8829 - loss: 0.2669 - val_accuracy: 0.8343 - val_loss: 0.3698
Epoch 3/5
[1m107/107[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m18s[0m 171ms/step - accuracy: 0.9314 - loss: 0.1794 - val_accuracy: 0.8390 - val_loss: 0.3783
Epoch 4/5
[1m107/107[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m18s[0m 172ms/step - accuracy: 0.9508 - loss: 0.1287 - val_accuracy: 0.8496 - val_loss: 0.3836
Epoch 5/5
[1m107/107[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m18s[0m 170ms/step - accuracy: 0.9760 - loss: 0.0757 - val_accuracy: 0.8555 - val_loss: 0.4293
[1m27/27[0m [32m━━━━━━━━━━━━━━━━━━━━[0m[37m[0m [1m1s[0m 52ms/step - accuracy: 0.8577 - loss: 0.4420
Pretrained Word2Vec Model Accuracy: 0.86

print(f"Trainable Embedding Model Accuracy: {trainable_acc:.2f}")
print(f"Pretrained Word2Vec Model Accuracy: {pretrained_acc:.2f}")

Trainable Embedding Model Accuracy: 0.85
Pretrained Word2Vec Model Accuracy: 0.86