An Integrated NPL Approach to Sentiment Analysis in Satisfaction Surveys

TL;DR

This paper proposes an integrated NLP approach to analyze satisfaction survey responses, focusing on sentiment, opinion mining, and pattern recognition to better understand participant feedback and improve satisfaction strategies.

Contribution

It introduces a comprehensive NLP-based framework combining sentiment analysis, opinion mining, and pattern recognition specifically for satisfaction survey data.

Findings

Effective identification of emotional polarity in responses

Enhanced understanding of key themes and trends

Potential to inform strategic improvements

Abstract

The research project aims to apply an integrated approach to natural language processing NLP to satisfaction surveys. It will focus on understanding and extracting relevant information from survey responses, analyzing feelings, and identifying recurring word patterns. NLP techniques will be used to determine emotional polarity, classify responses into positive, negative, or neutral categories, and use opinion mining to highlight participants opinions. This approach will help identify the most relevant aspects for participants and understand their opinions in relation to those specific aspects. A key component of the research project will be the analysis of word patterns in satisfaction survey responses using NPL. This analysis will provide a deeper understanding of feelings, opinions, and themes and trends present in respondents responses. The results obtained from this approach can be used to identify areas for improvement, understand respondents preferences, and make strategic decisions based on analysis to improve respondent satisfaction.