Constructing a Robust Sentiment Analysis Model with Custom Text Preprocessing using NLTK
It's me, Fidel Vetino aka "The Mad Scientist" bringing my undivided best from these tech streets... I create project structure so you can follow my coding step and process I follow to be successful @ FunctionTransformer: Build Robust Preprocessing Pipelines with Custom Transformations; Let's get right to it:
1. Project Structure:
First, let's outline a basic project structure:
kotlin
sentiment_analysis_project/
│
├── data/
│ └── dataset
│
├──
.csv
│
├── notebooks/
│ └── sentiment_analysis
│ └── dataset.csv
│
├── notebooks/
│ └── sentiment_analysis.ipynb
│
├── models/
│ └── sentiment
│ └── dataset.csv
│
├── notebooks/
│ └── sentiment_analysis.ip
│ └── dataset.csv
│
├── notebooks
│ └── dat
.ipynb
│
├── models/
│ └── sentiment_model.pkl
│
└── src/
├── preprocessing
├── preprocess
.py
└── sentiment_model.py
2. Sentiment Analysis Model Development:
Here's an outline of the steps to build and develop a sentiment analysis model:
Step 1: Data Preprocessing
python
import pandas as pd
from sklearn.model_selection import train_test_split
# Load data
data = pd.read_csv('data/dataset.csv')
# Train-test split
train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)
# Preprocessing (e.g., remove special characters, stopwords, stemming)
# Custom preprocessing can be done using libraries like NLTK or SpaCy
# Implement your preprocessing functions in preprocessing.py
from src.preprocessing import custom_preprocessing_function
train_data['text'] = train_data['text'].apply(custom_preprocessing_function)
test_data['text'] = test_data['text'].apply(custom_preprocessing_function)
Step 2: Feature Engineering
python
from sklearn.svm import LinearSVC
from sklearn.metrics import accuracy_score
# Initialize and train the model
svm_model = LinearSVC()
svm_model.fit(X_train, y_train)
# Evaluate the model
y_pred = svm_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
Step 3: Model Building and Training
python
from sklearn.svm import LinearSVC
from sklearn.metrics import accuracy_score
# Initialize and train the model
svm_model = LinearSVC()
svm_model.fit(X_train, y_train)
# Evaluate the model
y_pred = svm_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
Step 4: Save the Model
python
import joblib
# Save the model
joblib.dump(svm_model, 'models/sentiment_model.pkl')
3. FunctionTransformer with Custom Transformations:
Custom Preprocessing Function
Let's onpreprocessing.py:
python
from sklearn.base import BaseEstimator, TransformerMixin
class CustomPreprocessor(BaseEstimator, TransformerMixin):
def __init__(self):
pass
def fit(self, X, y=None):
return self
def transform(self, X):
# Implement custom preprocessing here
return X
Using FunctionTransformer in Pipeline
python
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
# Import custom preprocessing function
from src.preprocessing import CustomPreprocessor
# Create a pipeline with FunctionTransformer
pipeline = Pipeline([
('preprocessor', FunctionTransformer(CustomPreprocessor().transform)),
# Add more preprocessing steps or model training steps here
])
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4. Complex Preprocessing Workflows:
Let's say we have a complex preprocessing function that handles special characters, stopwords, and stemming:
python
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
import string
def complex_preprocessing(text):
# Remove punctuation
text = text.translate(str.maketrans('', '', string.punctuation))
# Tokenization
tokens = word_tokenize(text)
# Remove stopwords
stop_words = set(stopwords.words('english'))
tokens = [word for word in tokens if word.lower() not in stop_words]
# Stemming
stemmer = PorterStemmer()
tokens = [stemmer.stem(word) for word in tokens]
# Reconstruct text
processed_text = ' '.join(tokens)
return processed_text
Applying the Complex Preprocessing Function
python
# Assuming you have loaded your dataset into 'data'
data['text'] = data['text'].apply(complex_preprocessing)
These steps should give you a good starting point for building a sentiment analysis model with custom preprocessing using FunctionTransformer. Adjustments might be needed based on your specific dataset and requirements.
Here I just wanted to create another way to custom preprocessing pipeline using NLTK for removing special characters, stopwords, and performing stemming. We'll implement this in "preprocessing.py."
python
# preprocessing.py
import string
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
def remove_special_characters(text):
"""
Remove special characters from text.
"""
return ''.join(char for char in text if char not in string.punctuation)
def remove_stopwords(text):
"""
Remove stopwords from text.
"""
stop_words = set(stopwords.words('english'))
tokens = word_tokenize(text)
return ' '.join(word for word in tokens if word.lower() not in stop_words)
def perform_stemming(text):
"""
Perform stemming on text.
"""
stemmer = PorterStemmer()
tokens = word_tokenize(text)
return ' '.join(stemmer.stem(word) for word in tokens)
# Combine all preprocessing steps into a single function
def preprocess_text(text):
"""
Preprocess text by removing special characters, stopwords, and performing stemming.
"""
text = remove_special_characters(text)
text = remove_stopwords(text)
text = perform_stemming(text)
return text
Let's utilize this custom preprocessing pipeline in our sentiment analysis model development:
python
# sentiment_analysis.py
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
from sklearn.metrics import accuracy_score
from preprocessing import preprocess_text
# Load data
data = pd.read_csv('data/dataset.csv')
# Preprocess text
data['preprocessed_text'] = data['text'].apply(preprocess_text)
# Train-test split
train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)
# Vectorize text data
tfidf_vectorizer = TfidfVectorizer(max_features=1000)
X_train = tfidf_vectorizer.fit_transform(train_data['preprocessed_text'])
X_test = tfidf_vectorizer.transform(test_data['preprocessed_text'])
y_train = train_data['label']
y_test = test_data['label']
# Initialize and train the model
svm_model = LinearSVC()
svm_model.fit(X_train, y_train)
# Evaluate the model
y_pred = svm_model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# Save the model
import joblib
joblib.dump(svm_model, 'models/sentiment_model.pkl')
This script loads the dataset, preprocesses the text using the custom preprocessing pipeline defined in { preprocessing.py,} trains a LinearSVC model using TF-IDF features, evaluates the model, and saves it for later use.
-- In summary, this walkthrough demonstrated the construction of a sentiment analysis model incorporating custom text preprocessing with NLTK. We began by cleaning and normalizing the dataset, removing special characters, stopwords, and performing stemming to enhance the quality of the text data. Following this, we split the data into training and testing sets, and engineered features using TF-IDF vectorization.
Training a Linear Support Vector Classifier on the vectorized features yielded a model capable of predicting sentiment labels. By evaluating the model's accuracy on the test set, I gauged its performance and ensured its suitability for real-world applications.
Finally, I emphasized the importance of model persistence for future use and scalability. This process highlights the essential steps in developing a robust sentiment analysis system, leveraging custom preprocessing techniques to improve model effectiveness and interpretability
{Thank you for your attention and commitment to follow me}
Best regards,
Fidel Vetino
Solution Architect & Cybersecurity Analyst
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