jiyamaryjoseph / amazon-alexa-sentimental-analysis

This repository contains a project that leverages natural language processing (NLP) and deep learning techniques to perform sentiment analysis on Amazon Alexa customer reviews.

Amazon Alexa Sentiment Analysis

Overview

This repository contains a project that analyzes sentiment in Amazon Alexa customer reviews using natural language processing (NLP) and deep learning techniques. The project aims to classify sentiments expressed in reviews as positive, negative, or neutral, employing machine learning models such as LSTM networks for accurate classification.

Features

• Data Collection: Utilized a publicly available dataset of Amazon Alexa customer reviews.
• Data Preprocessing: Cleaned and prepared text data for model training.
• Exploratory Data Analysis (EDA): Investigated data distribution and characteristics.
• Model Training: Implemented LSTM and other machine learning models for sentiment classification.
• Model Evaluation: Assessed model performance using appropriate metrics.
• Visualization: Visualized results and insights gained from sentiment analysis.

Technologies Used

• Python: Primary programming language.
• Pandas, NumPy: Data manipulation and numerical computations.
• Matplotlib, Seaborn: Data visualization.
• Scikit-learn: Machine learning models and evaluation metrics.
• TensorFlow/Keras: Deep learning framework for building LSTM models.
• NLTK: Natural language processing tasks.