Overview This repository contains code for performing sentiment analysis on Amazon Alexa reviews using Natural Language Processing (NLP) techniques. The goal is to analyze the sentiment expressed in customer reviews of Amazon Alexa products and gain insights into customer satisfaction and feedback.

Dataset The dataset used for this analysis includes the following factors:

Rating: The rating given by customers (1 to 5 stars). Date: The date when the review was posted. Variation: The specific variation of the Amazon Alexa product. Verified Reviews: The text content of the customer review. Feedback: Binary feedback indicating positive (1) or negative (0) sentiment. Business Questions What is the overall sentiment of Amazon Alexa reviews? How does sentiment vary across different product variations? Is there any correlation between the rating and the sentiment expressed in the reviews? Are there any trends or patterns in customer sentiment over time? What are the most common positive and negative aspects mentioned in the reviews? Can we identify any specific keywords or phrases associated with positive or negative sentiment? Methodology Data Preprocessing: Cleaning and preprocessing the text data, including tokenization, removal of stopwords, and stemming. Sentiment Analysis: Utilizing machine learning or deep learning models to classify the sentiment of each review as positive or negative. Exploratory Data Analysis (EDA): Analyzing trends, patterns, and relationships within the data using visualizations and statistical techniques. Feature Engineering: Extracting features from the text data to improve model performance, such as word embeddings or TF-IDF. Model Evaluation: Assessing the performance of the sentiment analysis model using metrics like accuracy, precision, recall, and F1-score. Interpretation: Drawing actionable insights from the analysis to inform business decisions and strategies.