BSc IT Project Guide: Customer Support Sentiment Analysis
1. Introduction
The Customer Support Sentiment Analysis project aims to analyze the sentiment expressed in customer support tickets, emails, or chats. This system uses natural language processing (NLP) techniques to determine whether a message is positive, negative, or neutral. This can help organizations understand customer satisfaction, improve service quality, and prioritize responses.
2. Objectives
- Develop a web-based tool to upload and analyze customer
support interactions.
- Use sentiment analysis to classify messages into positive, negative, or
neutral.
- Visualize sentiment distribution over time or by department.
- Provide actionable insights to support teams and management.
3. Tools and Technologies
- Frontend: HTML, CSS, JavaScript
- Backend: Python (Flask/Django)
- NLP Library: NLTK or spaCy, Transformers (BERT)
- Data Storage: MySQL or MongoDB
- Visualization: Chart.js, Matplotlib or Seaborn
4. Methodology
1. Collect and clean historical customer support data.
2. Preprocess text (tokenization, stopword removal, stemming/lemmatization).
3. Apply sentiment analysis model (pre-trained or custom-trained).
4. Store results and analyze sentiment trends.
5. Build user-friendly dashboards and reports.
5. Expected Outcome
The final product will be a sentiment analysis platform that
can:
- Automatically classify customer support tickets.
- Provide sentiment trends and reports.
- Help support teams prioritize and improve response quality.
6. Challenges
- Handling slang or informal language in support tickets.
- Ensuring high accuracy in classification.
- Managing large volumes of unstructured text data.
7. Future Scope
- Integrate with live support systems like Zendesk or
Freshdesk.
- Add emotion detection or topic modeling features.
- Multilingual support for global applications.