Sentiment Analysis: A Comparison with Jargon

January 3, 2022

For anyone faced with choosing a sentiment extraction method, KD Nuggets offers a useful comparison in, “Sentiment Analysis API vs Custom Text Classification: Which One to Choose?” Data consultant and blogger Jérémy Lambert used a concrete dataset to demonstrate the pros and cons of each approach. For sentiment analysis, is team tested out Google Cloud Platform Natural Language API, Amazon Web Service Comprehend, and Microsoft Azure Text Analytics. Of those, Google looks like it performed the best. The custom text classification engines they used were Google Cloud Platform AutoML Natural Language and Amazon Web Service Comprehend Custom Classification. Lambert notes there are several other custom classification options they could have used, for example Monkey Learn, Twinwords, and Connexun. We observe no specialized solutions like Lexalytics were considered.

Before diving into the comparison, Lambert emphasizes it is important to distinguish between sentiment analysis and custom text classification. (See the two preceding links for more in-depth information on each.) He specifies:

“*Trained APIs [sentiment analysis engines] are based on models already trained by providers with their databases. These models are usually used to manage common use cases of : sentiment analysis, named entity recognition, translation, etc. However, it is always relevant to try these APIs before custom models since they are more and more competitive and efficient. For specific use cases where a very high precision is needed, it may be better to use AutoML APIs [custom text classification engines]. … AutoML APIs allow users to build their own custom model, trained on the user’s database. These models are trained on multiple datasets beforehand by providers.”

See the write-up for details on use cases, test procedures, performance results, and taxi-meter pricing. For those who want to skip to the end, here is Lambert’s conclusion:

“Both alternatives are viable. The choice between Sentiment Analysis API and Custom text classification must be made depending on the expected performance and budget allocated. You can definitely reach better performance with custom text classification but sentiment analysis performance remains acceptable. As shown in the article, sentiment analysis is much cheaper than custom text classification. To conclude, we can advise you to try sentiment analysis first and use custom text classification if you want to get better accuracy.”

Cynthia Murrell, January 3, 2022


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