Natural language processing for analyzing online customer reviews: a survey, taxonomy, and open research challenges

Area of Science:

• Computational Linguistics
• Information Science
• E-commerce Analytics

Background:

• E-commerce growth has increased reliance on online reviews for product assessment.
• Natural Language Processing (NLP) is crucial for analyzing vast amounts of customer feedback.
• Existing literature lacks a consolidated taxonomy of NLP applications in this domain.

Purpose of the Study:

• To establish a comprehensive taxonomy of NLP applications for analyzing online customer reviews.
• To review state-of-the-art methods, data sources, and research challenges in the field.
• To identify emerging trends and future research directions.

Main Methods:

• Systematic literature review of 154 publications from 2013-2023.
• Categorization of NLP applications into five key areas.
• Analysis of emerging techniques and data sources.

Main Results:

• A taxonomy classifying NLP applications into: sentiment analysis, review management, customer experience, user profiling, and marketing/reputation management.
• Dominance of Amazon user reviews in existing research.
• Increasing adoption of advanced NLP models like BERT and LSTM.

Conclusions:

• NLP is vital for understanding online customer reviews, with applications spanning multiple business functions.
• Future research should explore diverse data sources and advanced NLP techniques.
• The field shows significant growth and increasing academic interest.