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Sentiment Analysis- The Emotional aspect of Social Media, Website Reviews and Games — Part 10
Primer
As I discussed before, sentiment analysis is used to read emotions expressed as a text or as a voice data, using many techniques which include NLP, semantic search engines, dynamic classification, translation etc., which in turn takes us towards the opinion mining architecture, which I introduced in Part -1 of this series.

The image above is to kindle your memory. I will continue with dynamic classification, as I mentioned in my previous article.
Front End Modules
Dynamic classifiers
Opinion mining as mentioned earlier, is regularly used in product marketing as many users leave reviews about the products they purchased online or offline in the review page of the products. Also known as review mining or appraisal extraction, they are meant to understand the public mood in customer satisfaction, marketing strategy, sales prediction and market intelligence. Social media platforms play sufficiently important role in sentiment analysis , as many people comment on products, people, activities, places , events etc., Public experiences can also be found in web sites, group forums, community blogs, individual blogs etc., The semantic analysis in the opinion mining architecture extracts emotions /sentiments from the voice and text data, while the extracted emotions are classified into dynamic or static emotions using a dynamic classifier, as dynamic emotions are extensively used in gaming and interactive entertainment, extended reality applications such as VR, AR, and MR, healthcare applications such as pain and stress therapies, and many other academic and surgical applications.
To understand static and dynamic emotions( Positive, Negative and Neutral) , it is essential to understand time steps.
Static emotions are not time dependent , while dynamic emotions are extremely time- based. The duration of an emotion , plays an important role in the trigger alert system, as negative emotions such as hatred, jealousy, and anger…