6 Semantic Analysis Meaning Matters Natural Language Processing: Python and NLTK Book

Social platforms, product reviews, blog posts, and discussion forums are boiling with opinions and comments that, if collected and analyzed, are a source of business information. The more they’re fed with data, the smarter and more accurate they become in sentiment extraction. Can you imagine analyzing each of them and judging whether it has negative or positive sentiment? One of the most useful NLP tasks is sentiment analysis – a method for the automatic detection of emotions behind the text.

There’s also Brand24, digital marketing and advertising — some day I’d love to try the last one. This approach is easy to implement and transparent when it comes to rules standing behind analyses. Rules can be set around other aspects of the text, for example, part of speech, syntax, and more.

Building Blocks of Semantic System

Machine learning-based semantic analysis involves sub-tasks such as relationship extraction and word sense disambiguation. It’s an essential sub-task of Natural Language Processing and the driving force behind machine learning tools like chatbots, search engines, and text analysis. Using such a tool, PR specialists can receive real-time notifications about any negative piece of content that appeared online. On seeing a negative customer sentiment mentioned, a company can quickly react and nip the problem in the bud before it escalates into a brand reputation crisis.