Name of the team on GitHub Classroom:
Team Member Contributions:
Member 1
| Details | Comment |
|---|---|
| Student ID: | |
| Name: | |
| What part of the lab did you personally contribute to, and what did you learn from it? |
Member 2
| Details | Comment |
|---|---|
| Student ID: | |
| Name: | |
| What part of the lab did you personally contribute to, and what did you learn from it? |
Member 3
| Details | Comment |
|---|---|
| Student ID: | |
| Name: | |
| What part of the lab did you personally contribute to, and what did you learn from it? |
Member 4
| Details | Comment |
|---|---|
| Student ID: | |
| Name: | |
| What part of the lab did you personally contribute to, and what did you learn from it? |
Member 5
| Details | Comment |
|---|---|
| Student ID: | |
| Name: | |
| What part of the lab did you personally contribute to, and what did you learn from it? |
Your client, a university, is seeking to enhance their qualitative analysis of student course evaluations collected from students. They have provided you with a dataset containing student course evaluation for two courses in the Business Intelligence Option. The two courses are:
- BBT 4106: Business Intelligence I
- BBT 4206: Business Intelligence II
The client wants you to use Natural Language Processing (NLP) techniques to identify the key topics (themes) discussed in the course evaluations. They would also like to get the sentiments (positive, negative, neutral) of each theme in the course evaluation.
Lastly, the client would like an interface through which they can provide input in the form of new textual data (one student's textual evaluation at a time) and the output expected is:
- The topic (theme) that the new textual data is talking about.
- The sentiment (positive, negative, neutral) of the new textual data.
Use one of the following to create a demo interface for your client:
- Hugging Face Spaces using a Gradio App – https://huggingface.co/spaces
- Streamlit Community Cloud (Streamlit Sharing) using a Streamlit App – https://share.streamlit.io
Use the course evaluation dataset provided in class.
Provide a brief interpretation of the results and a recommendation for the client.
- Interpret what the discovered topics mean and why certain sentiments dominate
- Provide recommendations based on your results. Do not recommend anything that is not supported by your results.
Submit the link to a short video (not more than 4 minutes) demonstrating the topic modelling and the sentiment analysis. Also include (in the same video) the user interface hosted on hugging face or streamlit.
| Key | Value |
|---|---|
| Link to the video: | |
| Link to the hosted application: |
| Component | Weight | Description |
|---|---|---|
| Data Preprocessing & Analysis | 20% | Cleaning, preprocessing, and justification of chosen methods. |
| Topic Modelling | 20% | Correctness, interpretability, and coherence of topics. |
| Sentiment Analysis | 20% | Appropriate model choice and quality of sentiment classification. |
| Interface Design & Functionality | 20% | Usability, interactivity, and deployment success. |
| Interpretation & Recommendation | 10% | Logical, evidence-based, and actionable insights. |
| Presentation (Video & Clarity) | 10% | Clarity, professionalism, and demonstration of understanding. |