Finding the Tone of Your Customers' Voice – Sentiment Analysis
A common use case for NLP is sentiment analysis. Here, the goal is to identify the underlying emotion in some text, whether positive or negative, and all the nuances in between. Sentiment analysis is implemented in many fields, such as to analyze incoming messages, emails, reviews, recorded conversations, and other similar texts.
Generally, sentiment analysis belongs to a bigger group of NLP applications known as text classification. In the case of sentiment analysis, the goal is to predict the sentiment class.
Another common example of text classification is language detection. Here, the goal is to recognize the text language. In both cases, if we use an RNN for the task, we need to adopt a many-to-one architecture. A many-to-one neural architecture accepts a sequence of inputs at different times, 
, and uses the final state of the output unit to predict the one single class – that is, sentiment...
