Code Along for New Project

AIVIA Collaborations Code-Along Discussions
1

Sure! Let’s expand each of the steps with more detailed code and explanations, as if I’m mentoring you through the individual stages of this project.

Harnessing the Power of Sentiment Analysis: A Python NLP Project

Step 1: Data Collection

Task: Download the dataset.

Code:

import pandas as pd

# Specify URL of the dataset
url = 'https://path-to-your-dataset.csv'  # Replace this with the actual URL
# Load the dataset
data = pd.read_csv(url)

# Display first few rows of the dataset
print(data.head())

Explanation

Here, we first import the pandas library, which is essential for data manipulation in Python. We then specify the URL where the dataset can be found (you should replace the placeholder). Using pd.read_csv(), we retrieve the data and load it into a DataFrame. Finally, we print the first few rows to get an overview of the dataset structure—this helps to understand what columns we have and what type of data we’re dealing with.

Step 2: Data Exploration

Task: Explore the dataset to understand its structure and contents.

Code:

# Check for missing values
missing_values = data.isnull().sum()
print("Missing values in each column:\n", missing_values)

# View the distribution of sentiments
print("Distribution of Sentiments:\n", data['sentiment'].value_counts())

# Display basic statistics about the dataset
print(data.describe())

Explanation

In this step, we’re checking for missing values using isnull().sum(), which gives us the number of missing entries for each column. Then we use value_counts() to understand the distribution of target sentiments (like positive, negative, neutral). Finally, data.describe() gives us basic statistical information (mean, std, etc.) about numerical columns in the dataset, furthering our understanding of the data.

Step 3: Data Preprocessing

Task: Clean and preprocess textual data.

Code:

import nltk
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
import re

# Download the stopwords corpus
nltk.download('stopwords')
stop_words = set(stopwords.words('english'))
stemmer = PorterStemmer()

def preprocess_text(text):
    # Remove special characters and digits
    text = re.sub(r'[^a-zA-Z\s]', '', text)
    text = text.lower()  # Convert to lowercase
    # Tokenization and stop words removal
    tokens = text.split()
    tokens = [word for word in tokens if word not in stop_words]
    # Stemming
    tokens = [stemmer.stem(word) for word in tokens]
    return ' '.join(tokens)

# Apply preprocessing to the textual column
data['cleaned_text'] = data['text'].apply(preprocess_text)
print(data[['text', 'cleaned_text']].head())

Explanation

This step involves cleaning the textual data. We use the nltk library to import stop words and use a stemmer. The preprocess_text function performs several tasks:

  • It removes any non-alphabetic characters and digits.
  • Converts all text to lowercase for uniformity.
  • Tokenizes the text into individual words.
  • Removes stop words (common words like ‘and’, ‘the’, etc.) which don’t contribute much to sentiment.
  • Applies stemming using PorterStemmer, which reduces words to their base or root form.

Finally, we apply this function on the ‘text’ column to create a new ‘cleaned_text’ column for further processing.

Step 4: Feature Extraction

Task: Convert text to numerical features using Bag-of-Words and TF-IDF.

Code:

from sklearn.feature_extraction.text import TfidfVectorizer

# Initialize the TF-IDF Vectorizer
tfidf_vectorizer = TfidfVectorizer(max_features=5000)  # Limit to 5000 features
X = tfidf_vectorizer.fit_transform(data['cleaned_text']).toarray()  # Convert to array
y = data['sentiment']  # Target variable

print(X.shape)  # Show the dimensions of the feature matrix

Explanation

In this step, we use the TfidfVectorizer from Scikit-learn for feature extraction. TF-IDF (Term Frequency-Inverse Document Frequency) helps in converting the cleaned text into a numerical form that can be consumed by machine learning algorithms. The max_features parameter limits the number of features (words) to 5000 to avoid high-dimensionality problems. The fit_transform method constructs the TF-IDF matrix and converts it into an array format, assigning it to X. The sentiment column is assigned as y to denote the target variable.

Step 5: Train-Test Split

Task: Split the dataset into training and testing sets.

Code:

from sklearn.model_selection import train_test_split

# Split the data into training (80%) and testing (20%) sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Display the size of the splits
print(f'Training set size: {X_train.shape[0]}, Testing set size: {X_test.shape[0]}')

Explanation

Here, we use train_test_split from Scikit-learn to divide our dataset into training and testing subsets. We allocate 80% of the data for training and 20% for testing, which is a common practice. The random_state parameter ensures reproducibility of results. Printing the shapes provides a quick check of the sizes of our training and test sets.

Step 6: Building the Model

Task: Train a machine learning model (e.g., Logistic Regression).

Code:

from sklearn.linear_model import LogisticRegression

# Initialize the Logistic Regression model
model = LogisticRegression(max_iter=1000)  # Increase max_iter for convergence
# Train the model with training data
model.fit(X_train, y_train)

print("Model training completed.")

Explanation

In this step, we initialize the Logistic Regression model (an effective model for binary classification) and set max_iter to 1000 to ensure the model converges properly during training, especially if the dataset is large. We then train the model using the fit method with the training data (X_train, y_train). After training, we confirm with a print statement.

Step 7: Model Evaluation

Task: Evaluate the model using accuracy, confusion matrix, and classification report.

Code:

from sklearn.metrics import accuracy_score, classification_report, confusion_matrix

# Make predictions with test data
y_pred = model.predict(X_test)

# Calculate various evaluation metrics
accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)
class_report = classification_report(y_test, y_pred)

# Display the results
print(f'Accuracy: {accuracy:.2f}')
print('Confusion Matrix:\n', conf_matrix)
print('Classification Report:\n', class_report)

Explanation

We make predictions on the test set with model.predict() and store the results in y_pred. We then compute:

  • Accuracy: The ratio of correctly predicted instances to the total instances.
  • Confusion Matrix: A summary of prediction results showing true positives, false positives, true negatives, and false negatives.
  • Classification Report: This provides metrics such as precision, recall, and F1-score for a clearer understanding of model performance.

Step 8: Data Visualization

Task: Plot the confusion matrix and visualizations for better insights.

Code:

import matplotlib.pyplot as plt
import seaborn as sns

# Set up the confusion matrix for visualization
plt.figure(figsize=(10, 7))
sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', 
            xticklabels=['Negative', 'Positive'], 
            yticklabels=['Negative', 'Positive'])
plt.ylabel('Actual')
plt.xlabel('Predicted')
plt.title('Confusion Matrix')
plt.show()

Explanation

Here we use Matplotlib and Seaborn for visualization. We create a heatmap using sns.heatmap() to show the confusion matrix, making it easier to interpret model predictions at a glance. The annot=True parameter displays the number of occurrences in each cell, and fmt='d' displays the numbers as integers.

Step 9: Conclusion

Task: Summarize your findings and consider improvements.

Code:

# Summarized insights from the evaluation
print(f"The Logistic Regression model achieved an accuracy of {accuracy*100:.2f}% on the test set.")
# Future improvements
print("Consider experimenting with more complex models like SVM or Random Forest, and fine-tuning hyperparameters.")

Explanation

In this final part, we summarize the accuracy of the model. Identifying potential improvements in future iterations is vital. Suggestions could include trying more sophisticated models, tuning hyperparameters, or utilizing ensemble methods.

Step 10: Additional Thoughts

  • Explore ensemble methods for potential improvement.
  • Investigate and implement advanced NLP techniques like BERT or transformers for better accuracy.
  • Use hyperparameter tuning techniques such as Grid Search for optimization.

Final Words

This project likewise could further explore sentiment over time, different visualizations of sentiment distribution, and even applications in recommendation systems based on user sentiment toward products. Continue to experiment, enhancing what you’ve built upon, and enjoy the journey of data science!