Sentiment Analysis for Customer Feedback

Example Dataset:

Overview:

In this research project, students will apply advanced NLP techniques and statistical methods to analyze customer feedback data. The goal is to develop a sentiment analysis model that can classify customer reviews into positive, negative, or neutral sentiments, providing valuable insights for businesses.

Instructions:

1. Data Collection: Gather a dataset of customer reviews from a specific industry, such as hospitality or e-commerce. Ensure the dataset includes a variety of sentiments.
2. Preprocessing: Clean and preprocess the text data by removing stop words, punctuation, and performing tokenization and lemmatization.
3. Feature Extraction: Use statistical methods such as TF-IDF (Term Frequency-Inverse Document Frequency) or word embeddings to convert text data into numerical features.
4. Model Development: Implement a sentiment analysis model using machine learning algorithms such as Naive Bayes, Support Vector Machines (SVM), or deep learning models like Recurrent Neural Networks (RNNs) or Transformers.
5. Evaluation: Evaluate the model’s performance using metrics such as accuracy, precision, recall, and F1-score. Compare different models to determine the most effective approach.
6. Interpretation and Reporting: Interpret the results and discuss the implications for business decision-making. Document the entire process, findings, and insights according to the Research Project Rubric.

**** Make note that you will create a presentation on your research for Case Study #2.

Submission Requirements:

A formal research paper (PDF or DOCX) that includes the following sections:

• Abstract: Summary of the research objectives, methodology, and findings.
• Introduction: Background, relevance of sentiment analysis in the selected industry, and research objectives.
• Data Collection:
• Source and description of dataset.
• Industry focus (e.g., hospitality, e-commerce).
• Summary statistics of the dataset (e.g., number of reviews, distribution of sentiments).
• Data Preprocessing:
• Description of cleaning steps (e.g., stop word removal, lemmatization).
• Justification for preprocessing techniques used.
• Feature Extraction:
• Method used (TF-IDF, Word2Vec, BERT embeddings, etc.).
• Visualization or description of feature space (optional).
• Model Development:
• Algorithms used (e.g., Naive Bayes, SVM, RNN, Transformer).
• Rationale for model selection.
• Hyperparameters and training strategy.
• Model Evaluation:
• Performance metrics (Accuracy, Precision, Recall, F1-score).
• Comparison of different models.
• Confusion matrix and/or ROC curves (if applicable).
• Interpretation and Discussion:
• Business insights derived from the results.
• Limitations and potential improvements.
• Conclusion:
• Summary of key findings and implications for business decision-making.
• References: Use APA or IEEE citation style.
• Appendices (if applicable): Additional figures, tables, or code snippets.

2. Codebase (ZIP or GitHub link)

• Well-documented Python code or Jupyter Notebook including:
• Data loading and preprocessing scripts.
• Feature extraction modules.
• Model training and evaluation scripts.
• Inline comments and markdown explanations.
• ReadMe file explaining how to run the project and reproduce results.