通过基于社交媒体的分析比较Word2Vec与BERT词嵌入进行新产品开发（NPD）
New Product Development (NPD) through Social Media-based Analysis by Comparing Word2Vec and BERT Word Embeddings
AI总结：
本文比较Word2Vec与BERT词嵌入在有限数据下对推文情感和观点分类的效果，发现BERT结合Balanced Random Forest表现最佳，并通过词图分析为新产品开发提供改进反馈。
摘要：
本研究引入了用于推文情感与观点分类的新方法，以支持新产品开发（NPD）流程。研究评估了两种流行的词嵌入技术Word2Vec和BERT，将其作为经典机器学习与深度学习算法的输入，以在有限数据条件下识别情感分析和观点检测中表现最佳的方法。结果表明，在一个用例上，BERT词嵌入与Balanced Random Forest相结合，在情感分析和观点检测两方面均产生了最准确的单一模型。此外，本文还通过对具有相同情感的推文进行词图分析，为未来产品开发提供反馈，以突出潜在的改进领域。
英文摘要：
This study introduces novel methods for sentiment and opinion classification of tweets to support the New Product Development (NPD) process. Two popular word embedding techniques, Word2Vec and BERT, were evaluated as inputs for classic Machine Learning and Deep Learning algorithms to identify the best-performing approach in sentiment analysis and opinion detection with limited data. The results revealed that BERT word embeddings combined with Balanced Random Forest yielded the most accurate single model for both sentiment analysis and opinion detection on a use case. Additionally, the paper provides feedback for future product development performing word graph analysis of the tweets with same sentiment to highlight potential areas of improvement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
