Enhancement of BERT Model for Consumer Sentiment Analysis in E-commerce
Keywords:
Deep Learning, Customer Sentiment, E-commerce, RNNs, CNNs, BERT, NFTAbstract
An abundance of textual data, including important insights into consumer feelings regarding items and services, has been created by the expansion of online purchasing platforms. Deep learning techniques have emerged as powerful tools for automatically extracting sentiment information from such data. These avenues for future exploration aim to advance sentiment analysis capabilities in e-commerce, fostering better understanding of customer preferences and driving improvements in product offerings and user experiences. This paper explores the current landscape and future prospects of deep learning approaches for sentiment analysis in e-commerce. This paper presents an enhanced BERT model tailored for consumer sentiment analysis in e-commerce contexts. Leveraging the powerful capabilities of BERT, our approach encompasses a structured workflow encompassing NFT data collection, preprocessing, tokenization, training, and testing, culminating in comprehensive model evaluation. The architecture of our model incorporates a pre-trained BERT model alongside a specialized classification layer, optimizing performance for sentiment classification tasks. To mitigate risks of overfitting, we employ techniques such as Early Stopping and ModelCheckpoint during training. Following training, performance metrics are rigorously assessed on a dedicated testing set to ensure model robustness and efficacy. Ultimately, the trained model is seamlessly integrated into the e-commerce platform, augmenting customer experience and empowering informed decision-making processes. Our goal is to improve the effectiveness and precision of online sentiment analysis so that businesses may get a better understanding of their customers' opinions and preferences.
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