Sentiment Analysis and Chatbot Development for Kenya Power and Lighting Company (KPLC)

Business Understanding

Overview

Kenya Power and Lighting Company (KPLC) is a major utility provider that receives a high volume of customer feedback through social media platforms, particularly Twitter. Understanding customer sentiment is crucial for KPLC to improve its services, automate responses, and enhance customer satisfaction. The project aims to develop a sophisticated chatbot that can classify various types of tweets and generate appropriate automated responses.

Problem Statement

KPLC faces the challenge of efficiently processing and categorizing customer feedback from social media, especially Twitter, where customers express their sentiments about KPLC's services. By accurately classifying tweets into sentiment categories, KPLC can identify common complaints, pinpoint service issues, and enhance customer feedback mechanisms. This will enable KPLC to improve service quality, response times, and overall customer experience.

Objectives

1. Gauge Overall Customer Sentiment: Understand the general sentiment towards KPLC's services to identify areas for improvement.
2. Identify Specific Issues: Detect and categorize specific issues mentioned in tweets, such as power outages, billing problems, and token issues.
3. Create a Responsive Chatbot: Develop a chatbot that can provide appropriate responses to customer inquiries, improving customer service efficiency and response times.

Challenges

1. Data Collection and Preprocessing: Gathering relevant tweets mentioning KPLC, cleaning and preprocessing the data, and handling noise like unrelated tweets and abbreviations.
2. Sentiment Analysis Accuracy: Dealing with informal language, sarcasm, mixed sentiments, and local dialects often used on social media platforms.
3. Identifying Specific Issues: Extracting and categorizing specific issues mentioned in tweets can be complex due to diverse customer descriptions.
4. Real-time Data Processing: Processing a continuous stream of tweets in real-time to provide timely insights and responses.
5. Handling Multilingual and Local Dialects: Dealing with tweets in multiple languages and local dialects that complicate sentiment analysis and issue detection.
6. Evaluating Model Performance: Ensuring models perform well across different contexts, languages, and over time requires ongoing evaluation and tuning.

Data Understanding

Data Sources

The project will source data primarily from social media platforms like Twitter, customer feedback forms, surveys, and direct messages to KPLC's official accounts. This diverse data collection approach will provide a comprehensive view of customer sentiment and service issues.

Relevance of the Data

The data collected from various sources is essential for understanding customer sentiment, identifying common issues, and improving service quality. Analyzing this data will provide valuable insights into customer perceptions, help in addressing specific concerns, and guide the development of the chatbot for efficient customer interactions.

Proposed Solution

Tools and Techniques

• Advanced Natural Language Processing (NLP) Techniques: Utilizing NLP for text analysis, sentiment classification, and issue detection.
• Data Cleaning Scripts: Developing scripts to filter out irrelevant data, handle misspellings, and normalize text for consistent analysis.
• Machine Learning Models: Training sentiment analysis models using supervised learning with labeled datasets to improve accuracy.
• Real-time Data Streaming Tools: Implementing tools for processing a continuous stream of tweets in real-time for timely insights.
• Chatbot Frameworks: Utilizing frameworks like Rasa for developing the chatbot integrated with sentiment analysis and issue categorization models.
• Multilingual NLP Models: Incorporating multilingual NLP models and fine-tuning them with local dialect data for accurate analysis.
• Continuous Model Evaluation: Establishing a framework for ongoing evaluation and tuning of models to ensure performance consistency.

Metrics of Success

• Sentiment Accuracy: Percentage of correctly classified sentiments (positive, negative, neutral) to gauge customer sentiment effectively.
• Issue Detection Rate: Number of key issues identified and addressed based on sentiment analysis to improve service quality.
• Machine Learning Model Accuracy: Training a machine learning model with an accuracy of 85% and above to ensure reliable sentiment analysis.

Conclusion

The project aims to enhance KPLC's customer service by analyzing customer feedback from social media, improving sentiment analysis accuracy, and developing a responsive chatbot for customer inquiries. Despite the challenges, sentiment analysis and chatbot development are powerful tools for understanding customer sentiment, addressing service issues, and ultimately increasing customer satisfaction levels.