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:

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.
Preprocessing: Clean and preprocess the text data by removing stop words, punctuation, and performing tokenization and lemmatization.
Feature Extraction: Use statistical methods such as TF-IDF (Term Frequency-Inverse Document Frequency) or word embeddings to convert text data into numerical features.
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.
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.
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.

WRITE MY PAPER

Sentiment Analysis for Customer Feedback