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.

翻译：本研究介绍了基于推特的情感和意见分类的新方法，旨在支持新产品开发（NPD）流程。两种流行的词嵌入技术，Word2Vec和BERT，被评估为经典机器学习和深度学习算法的输入，以找到情感分析和意见检测方面性能最佳的方法。结果表明，结合平衡随机森林方法的BERT词嵌入模型在使用案例中为情感分析和意见检测提供了最准确的单一模型。此外，本文通过对具有相同情感的推文进行单词图分析，为未来的产品开发提供了反馈，以突出潜在的改进方向。