In this project, a sentiment analysis model was developed to analyze Amazon Alexa reviews using NLP techniques and machine learning classifiers. Through data preprocessing, model training, and deployment, actionable insights were derived to understand customer sentiments effectively.
Step 1. Data Collection: Reviews of Amazon Alexa were collected from various online sources, such as e-commerce websites, forums, and social media platforms.
Step 2. Data Preprocessing: The collected reviews were preprocessed to prepare them for analysis. This involved steps such as removing special characters, converting text to lowercase, tokenization (splitting text into individual words), stemming (reducing words to their root form), and removing stopwords (commonly used words like 'the', 'is', 'and' that do not carry significant meaning).
Step 3. Exploratory Data Analysis (EDA): Exploratory data analysis was performed to gain insights into the dataset. This included analyzing the distribution of review sentiments (positive, negative) and visualizing the most frequent words using word clouds.
Step 4. Feature Engineering: Text features were extracted from the preprocessed reviews to represent them in a format suitable for machine learning models. This involved techniques such as bag-of-words representation or TF-IDF (Term Frequency-Inverse Document Frequency) encoding.
Step 5. Model Building: Several machine learning classifiers were trained on the labeled dataset to predict the sentiment of reviews. Popular classifiers such as XGBoost, Random Forest, and Decision Tree were used. The dataset was split into training and testing sets to evaluate the performance of the models.
Step 6. Model Evaluation: The trained models were evaluated using performance metrics such as accuracy, precision, recall, and F1-score. Cross-validation techniques were employed to ensure robustness and generalization of the models.
Step 7. Deployment and Visualization: The best-performing model was selected and deployed to analyze new reviews of Amazon Alexa in real-time. Results were visualized using interactive dashboards or plots to provide actionable insights to stakeholders.