Decoding Consumer Sentiments: Advanced NLP Techniques for Analyzing Smartphone Reviews

Objectives: this study aims to bridge the gap in effectively analyzing online consumer feedback on smartphones, which is often voluminous and linguistically complex. The ultimate goal is to provide smartphone manufacturers with actionable insights to refine product features and marketing strategies. We propose a dual-model framework using bidirectional encoder representations from transformers (BERT) and sentence transformers for sentiment analysis and topic modeling, respectively. This approach is intended to enhance the accuracy and depth of consumer sentiment analysis. Method: sentiment analysis and topic modeling are applied to a large dataset of smartphone reviews sourced from Kaggle and Amazon. The BERT model is used to understand the context and sentiment of words, while sentence transformers generate embeddings for clustering reviews into thematic topics. Results: our analysis revealed strong positive sentiments regarding smartphone performance and user experience, while also identifying concerns about camera and battery life. However, while the model effectively captures positive feedback, it may struggle with negative feedback and especially neutral sentiments, due to the dataset’s bias toward positive reviews. Conclusions: the application of BERT and sentence transformers provides a significant technological advancement in the field of text analysis by enhancing the granularity of sentiment detection and offering a robust framework for interpreting complex data sets. This contributes to both theoretical knowledge and practical applications in digital consumer analytics