Fall 2023 Term 2 Project - AAPL Stock Sentiment Text Analysis

Please note - the Reddit Comments file could not be included in the repository due to being 125mb

Project Details:

Overview

This project is designed to showcase your group’s proficiency in data management, analysis, and visualization with a specific focus on sentiment analysis, exploratory data analysis (EDA), and statistical modeling using R, PowerBI, and Tableau. Unique dataset selection adds a competitive edge to your project experience.

Objectives

1. Select a unique dataset for your group to apply data management, analysis, and visualization techniques.

2. Conduct comprehensive sentiment analysis, EDA, and statistical modeling.

3. Develop and demonstrate skills in R, PowerBI, and Tableau.

4. Effectively present your analysis and findings in a 15-minute presentation

5. Dataset Selection Rules

The class is divided into 6 groups. Each group will choose one dataset from the 19 options provided below. Each dataset consists of multiple CSV files You must use all the files associated with the dataset that you choose. No two groups can use the same dataset.

Claiming a Dataset:

· A group member must announce their chosen dataset on the course's Canvas discussion board.

· Once a dataset is claimed, it becomes unavailable to other groups.

Penalty for Duplication:

· A 20% deduction in the project score will be applied to groups duplicating dataset choices.

2. Project Requirements

Data Exploration and Analysis:

Conduct a thorough EDA. Your EDA should include basic statistical descriptions, identifying trends or patterns. Interpret your findings and discuss their implications in the context of the problem.

Data Management:

· Detail your process of data extraction, cleaning, and preparation.

· Explain your rationale for any data transformation or data integration techniques employed.

Text Analysis:

· Text Preprocessing: Implement techniques like tokenization, stemming, or lemmatization as needed.

· Conduct Sentiment Analysis: Use R to perform sentiment analysis on relevant textual data within your dataset.

· Topic Modeling

· Topic Modeling :

· Interpretation: Discuss the implications of your text analysis results in the context of your dataset.

Statistical Modeling:

· Apply at least one statistical modeling technique relevant to your dataset and objectives.

· Clearly explain the choice of model, the assumptions made, and interpret the model’s results.

· Interpretation: Discuss the implications of your results in the context of your dataset.

Data Visualization:

· Develop advanced visualizations that clearly communicate your findings.

· Use PowerBI, Tableau, or R to create these visualizations.

· Ensure your visualizations are not only informative but also adhere to best practices in terms of design and clarity.

3.Deliverables

Final Report:

Use the final project template provided below to submit your project

4.Presentation

· Submit the presentation slides and detailed speaker notes as an appendix of your final report.