Sentiment Analysis API VS Custom Text Classification: which one to choose?

The article explores the comparative performance of Sentiment Analysis engines and Custom Text Classification engines using a specific dataset, highlighting their respective advantages and drawbacks. Sentiment Analysis, a natural language processing technique, determines whether textual data is positive, negative, or neutral, often utilized by businesses to gauge customer sentiment. In contrast, Text Classification assigns predefined categories to texts and requires training with labeled data. The study utilized various sentiment analysis and custom text classification engines from providers like Google Cloud Platform, AWS, and Microsoft Azure, accessed via the Eden AI platform. Results indicated that while custom text classification can achieve higher precision, sentiment analysis remains a cost-effective option, being significantly cheaper due to its pay-per-inference pricing model. The article concludes that the choice between the two should depend on performance expectations and budget, suggesting starting with sentiment analysis and using custom classification for greater accuracy.

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