Evaluating the noise tolerance of Cloud NLP services across Amazon, Microsoft, and Google

Natural Language Processing (NLP) has revolutionized industries, streamlining customer service through applications in healthcare, finance, legal, and human resources domains, and simplifying tasks like medical research, financial analysis, and sentiment analysis. To avoid the high costs of building and maintaining NLP infrastructure, companies turn to Cloud NLP services offered by major cloud providers like Amazon, Google, and Microsoft. However, there is little knowledge about how tolerant these services are when subjected to noise. This paper presents a study that analyzes the effectiveness of Cloud NLP services by evaluating the noise tolerance of sentiment analysis services provided by Amazon, Google, and Microsoft when subjected to 12 types of noise, including syntactic and semantic noises. The findings indicate that Google is the most tolerant to syntactic noises, and Microsoft is the most tolerant to semantic noises. These findings may help developers and companies in selecting the most suitable service provider and shed light towards improving state-of-the-art techniques for effective cloud NLP services.