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This study introduces novel methods for sentiment and opinion classificationof tweets to support the New Product Development (NPD) process. Two popularword embedding techniques, Word2Vec and BERT, were evaluated as inputs forclassic Machine Learning and Deep Learning algorithms to identify thebest-performing approach in sentiment analysis and opinion detection withlimited data. The results revealed that BERT word embeddings combined withBalanced Random Forest yielded the most accurate single model for bothsentiment analysis and opinion detection on a use case. Additionally, the paperprovides feedback for future product development performing word graph analysisof the tweets with same sentiment to highlight potential areas of improvement.

New Product Development (NPD) through Social Media-based Analysis by Comparing Word2Vec and BERT Word Embeddings