# Enhancing Product Design through AI-Driven Sentiment Analysis of Amazon Reviews Using BERT
## BERT  
In natural language processing (NLP), Google's groundbreaking Bidirectional Encoder Representations from Transformers (BERT) model has emerged as a pivotal framework. BERT's advancement lies in its pre-training phase, where it ingests an extensive corpus comprising 2500 million unlabeled words from Wikipedia and an additional 800 million from the Book Corpus. This pre-training methodology involves unsupervised learning tasks that entail predicting masked words within sentences and discerning logical coherence between pairs of sentences. The essence of BERT's innovation is its bidirectional context understanding; it comprehends context from both the left and right sides of words within sentences. Leveraging its pre-training on colossal text datasets, BERT exhibits adaptability by fine-tuning domain-specific datasets for distinct tasks. This fine-tuning process involves initializing the BERT model with pre-learned parameters tailored explicitly to labeled downstream tasks, such as sentiment analysis.  
BERT's significance in the NLP landscape is underscored by its robust pre-training on extensive textual resources, enabling it to grasp nuanced linguistic contexts. Moreover, its adaptability via fine tuning on domain-specific datasets is a testament to its versatility across various NLP applications, positioning BERT as a cornerstone framework in language understanding and analysis.  
Several recent studies have showcased the efficacy of BERT in diverse applications within natural language processing (NLP). [PERSON] et al. [19] developed the BERT-IAN model, specifically enhancing aspect-based sentiment analysis on datasets related to restaurants and laptops. Their model, utilizing a BERT-pretrained framework, separately encodes aspects and contexts. Through a transformer encoder, it adeptly learns the attention mechanisms of these aspects and contexts, achieving sentiment polarity accuracies of 0.80 for laptops and 0.74 for restaurants. [PERSON] et al. [20] proposed a multiclass classifier leveraging the BERT model on SemEval 2016 benchmark datasets. Their approach effectively predicted sentiment and aspects within reviews, employing multiple classifiers combining sentiment and aspect classifiers using BERT. In a study by [PERSON] et al. [21], the utilization of BERT in market segmentation analysis based on customer expectations of product features was explored. By deploying a BERT classification model on apparel product data, the analysis focused on understanding user attention toward specific product features such as pattern, price, cases, brand, fabric, and size. This analysis provided valuable insights for companies adapting to the apparel market. [PERSON] et al. [22], applied a BERT-based approach to comprehend contextual elements (Task, User, Environment) from online reviews. Context of Use (COU) is pivotal in successful User Experience (UX) analysis. Their study extracted COU elements using the BERT model on a scientific calculator dataset, elucidating the tasks associated with the product and the diverse user types and environments in which these calculators are utilized.  
The literature found in [12; 14; 16] revealed that the ABSA and NLP computational mechanisms can still be exploited for text mining and automation of content analysis. Collectively [20; 21; 22], these studies underscore the versatility and effectiveness of BERT in various NLP domains, from aspect-based sentiment analysis to understanding customer reviews. Based on the advantages of BERT, we propose an aspect-based sentiment analysis approach to automate the extraction of customer expectations from online reviews to provide valuable data-driven insight to product designers.  
The objective of this paper is to construct an NLP pipeline that automates the extraction of customer expectations from Amazon online reviews across various product categories. This will involve implementing aspect-based sentiment analysis to predict and categorize the aspects discussed within the reviews, while simultaneously classifying their sentiments.