Sentiment Analysis is a procedure used to determine if a chunk of text is positive, negative or neutral. In text analytics, natural language processing (NLP) and machine learning (ML) techniques are combined to assign sentiment scores to the topics, categories or entities within a phrase.
It’s a true story that Germans love their long words. However, this fact may not be so loved for text processing procedures. The lack of NLP libraries in Python adapted to German makes it difficult to properly analyze this kind of words. Let us share with you our NLP tool to split word compounds. It will transform the AI market.
A successful virtual agent must be able to hold a natural conversation with a human. Processing natural language is, however, not a piece of cake for Artificial Intelligence. The way we speak is not regular and robotic, but emotional and full of context. NLU becomes, therefore, essential for customer support bots to get what clients need.
A ‘word embeddings’ approach has been widely adopted for machine learning processes. While an extensive research has been carried out during these years to analyze all theoretical underpinnings of algorithms such as word2vec, GloVe or fastText, it is surprising that little has been done, in turn, to solve some of the more complex linguistic issues raised when getting down to business.
Machine learning algorithms require a great amount of numeric data to work properly. Real people, however, do not speak to bots using numbers, they communicate through the natural language. That’s the main reason why chatbot developers need to convert all these words into digits so that those virtual assistants can understand what users are saying. And here is where word embeddings come into play.