Data Preparation: Gather and preprocess the data. This involved tasks such as cleaning the text, removing stopwords, and converting the text into numerical form using a tokenizer.
Vectorization: Use a vectorizer, such as CountVectorizer or TfidfVectorizer, to convert the text data into a numerical feature matrix that can be used as input to a machine learning algorithm.
Feature Transformation: Apply a feature transformer, such as TruncatedSVD or PCA, to reduce the dimensionality of the feature matrix and improve the efficiency and effectiveness of the classification algorithm.
SMOTE: If the data is imbalanced, use the Synthetic Minority Over-sampling Technique (SMOTE) to balance the data by generating synthetic samples of the minority class.
Classification Algorithm: Train a classification algorithm, such as Logistic Regression, Naive Bayes, or Support Vector Machine, to classify the sentiment of the text data.
Hyperparameter Tuning: Fine-tune the hyperparameters of the classification algorithm using cross-validation and grid search techniques to improve the accuracy of the model.
Model Evaluation: Evaluate the performance of the model on a held-out test set using metrics such as accuracy, precision, recall, and F1 score.
Data Preparation: Gather and preprocess the data. This involved tasks such as cleaning the text, removing stopwords, and converting the text into numerical form using a tokenizer. Vectorization: Use a vectorizer, such as CountVectorizer or TfidfVectorizer, to convert the text data into a numerical feature matrix that can be used as input to a machine learning algorithm. Feature Transformation: Apply a feature transformer, such as TruncatedSVD or PCA, to reduce the dimensionality of the feature matrix and improve the efficiency and effectiveness of the classification algorithm. SMOTE: If the data is imbalanced, use the Synthetic Minority Over-sampling Technique (SMOTE) to balance the data by generating synthetic samples of the minority class. Classification Algorithm: Train a classification algorithm, such as Logistic Regression, Naive Bayes, or Support Vector Machine, to classify the sentiment of the text data. Hyperparameter Tuning: Fine-tune the hyperparameters of the classification algorithm using cross-validation and grid search techniques to improve the accuracy of the model. Model Evaluation: Evaluate the performance of the model on a held-out test set using metrics such as accuracy, precision, recall, and F1 score.
We implemented the following code for this task: https://github.com/fardinafdideh/Text-analytics/blob/main/sentiment-analysis-of-products-review-on-amazon.ipynb