How to Apply Natural Language Processing Models to Understand Your Reviews

In this paper, we explain the capabilities of Natural Language Processing (NLP) to analyze reviews and contribute to business intelligence. This resource is valuable for data analysts, product managers, mobile growth practitioners, and anyone with an interest in translating review data into strategic insights that can inspire product or service improvements that yield growth. 

 

In the paper we introduce two NLP models, Bidirectional Encoder Representations from Transformers (BERT) and Zero-Shot Learning (ZSL). BERT helps computers understand the meaning of ambiguous words and language in text by using the surrounding text to establish context. ZSL classification on the other hand, is an NLP inference model that helps group unlabeled data into unique label identifiers. BERT in particular is recognized as one of the best models for sentiment analysis and deep bidirectional understanding of text context. 

While the advent of AI promises a radical shift in how businesses approach reviews, Natural Language Processing models form one of the basis that powers the change. Even in the age of AI, Natural Language Processing (NLP) models like Bidirectional Encoder Representations from Transformers (BERT) and Zero-Shot Learning (ZSL) remain incredibly useful for making sense of review data. That’s because different NLP models can lead to varying results, so using and fine-tuning NLP models directly might be the key to capturing subtle and nuanced insights and reach the highest accuracy and quality.