Sentiment Analysis and Customer Satisfaction Factors Based on LSTM and Topic Modeling

Sakshi Nazirkar; Siddhivinayak Kulkarni

doi:10.17485/IJST/v16i28.1109

Article

Sentiment Analysis and Customer Satisfaction Factors Based on LSTM and Topic Modeling

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i28.1109

Year: 2023, Volume: 16, Issue: 28, Pages: 2126-2132

Original Article

Sentiment Analysis and Customer Satisfaction Factors Based on LSTM and Topic Modeling

Sakshi Nazirkar^1*, Siddhivinayak Kulkarni¹

¹School of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University, Pune, 411038, Maharashtra, India

*Corresponding Author
Email: [email protected]

Received Date:12 May 2023, Accepted Date:19 June 2023, Published Date:22 July 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: To predict sentiment of the Airbnb text reviews using Long Short Term Memory (LSTM). To improve the accuracy and performance metrics. To identify customer satisfaction and dissatisfaction factors of the Airbnb customers using Sentiment Analysis and Topic Modeling. Method: The study is divided into two parts after performing necessary pre-processing steps. First part focuses on sentiment analysis using LSTM. Dataset is created by combining review data of 3 cities, then, operations like pre-processing, sentiment analysis, label column creation, under sampling etc. were conducted. After this, data was trained on the configured LSTM Model. The second part of the study was Topic Modeling after applying Sentiment Analysis, on an Airbnb dataset, to derive and understand customer satisfaction and dissatisfaction factors.Findings: Sentiment Analysis using LSTM showed training accuracy of 96.37%.and testing accuracy of 93.89%. The performance metrics showed promising results. The topics found for negative and positive sentiment portraying the customer satisfaction and dissatisfaction factors after Topic Modeling align with the existing literature findings and are important to generalize the existing literature as well. Novelty: Improved performance metrics like Accuracy, F1- score and Recall for sentiment analysis using LSTM. Results stating customer satisfaction and dissatisfaction factors add value to the existing literature and help to generalize findings.

Keywords: Natural Language Processing; Sentiment Analysis; Topic Modeling; Deep Learning; LSTM; Customer Satisfaction

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Copyright

© 2023 Nazirkar & Kulkarni. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)