Sentiment Analysis of Structured and Unstructured Employee Feedback in a Multi-Tenant Environment Using Machine Learning Algorithms.

Description

This study addresses the difficulty in manually analyzing and quantifying large volumes of qualitative employee feedback to extract actionable organizational insights. Titled "Kobo Sentiment Analyzer", it aims to automate the evaluation of employee satisfaction and identifying key themes in questionnaire responses using Natural Language Processing (NLP) techniques including VADER for sentiment analysis and BERTopic for topic modeling. Designed for Human Resources (HR) departments and organizational managers, the system processes text-based feedback to visualize sentiment trends, correlations, and feature importance. It is expected to provide real-time, objective insights into workforce morale, contributing to data-driven decision-making for improving organizational culture and employee retention.