![]() ![]() ![]() Based on a meta-analysis of 272 datasets and 12 million sentiment-labeled text documents, we find that the recently proposed transfer learning models indeed perform best, but can perform worse than popular leaderboard benchmarks suggest. We propose an empirical framework and quantify these trade-offs for different types of research questions, data characteristics, and analytical resources to enable informed method decisions contingent on the application context. In contrast, machine learning methods are more complex to interpret, but promise higher accuracy, i.e., fewer false classifications. Lexicons can relate individual words and expressions to sentiment scores. Various sentiment analysis methods are available and new ones have recently been proposed. ![]() Countless marketing applications mine opinions from social media communication, news articles, customer feedback, or corporate communication. Sentiment is fundamental to human communication. ![]()
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