Support Ticket Sentimental Analysis

This project involves analyzing support tickets to understand the sentiment expressed by customers.

_{Pic Credit: Toa Heftiba - Unsplash}

Table of Contents

• Introduction
• Dataset
• Notebook Features
• Technologies
• Contributing
• License

Introduction

Support ticket systems generate vast amounts of customer feedback.

This project applies sentiment analysis using machine learning and NLP methods to classify support ticket text into positive, negative, or neutral sentiments.

By employing this techniques, this analysis aims to categorize and extract insights from customer messages to improve support service and overall customer satisfaction.

Dataset

The dataset contains 8.4k samples of the purchase history of various tech products. It consists of customer inquiries related to hardware issues, software bugs, network problems, account access, data loss, and other support topics.

Source: https://www.kaggle.com/datasets/suraj520/customer-support-ticket-dataset/data

Notebook Features

• Text Preprocessing: Cleaning and tokenization of support ticket text data.
• Sentiment Classification: Utilizing machine learning models to classify sentiments expressed in customer messages.
• Visualization: Visual representation of sentiment distribution or trends within support tickets.
• Insights and Recommendations: Extracting actionable insights to improve support team efficiency and customer satisfaction.

Notebook: Access here

Technologies

• Programming Language: Python
• Libraries: NLTK, Scikit-learn, Pandas, Matplotlib, Seaborn
• Tools: Jupyter Notebook

Contributing

Contributions are welcome! Feel free to submit issues or pull requests. For any inquiries or collaboration opportunities, you can reach out to https://www.linkedin.com/in/andre-kuster/

License

This project is licensed under the MIT License.