Topic analysis is a Natural Language Processing (NLP) technique that allows us to automatically extract meaning from text by identifying recurrent themes or topics.

Topic analysis (also called topic detection, topic modelling, or topic extraction) is a machine learning technique that organizes and understands large collections of text data, by assigning “tags” or categories according to each individual text’s topic or theme.

Topic analysis uses natural language processing (NLP) to break down human language so that you can find patterns and unlock semantic structures within texts to extract insights and help make data-driven decisions. The two most common approaches for topic analysis with machine learning are NLP topic modelling and NLP topic classification.

Topic modelling is an unsupervised machine learning technique. This means it can infer patterns and cluster similar expressions without needing to define topic tags or train data beforehand. This type of algorithm can be applied quickly and easily, but there’s a downside – they are rather inaccurate.

Text classification or topic extraction from text, on the other hand, needs to know the topics of a text before starting the analysis, because you need to tag data in order to train a topic classifier. Although there’s an extra step involved, topic classifiers pay off in the long run, and they’re much more precise than clustering techniques.

Our Model :

topic analyser is an intent classification model created by Supernova-PulsarAIGeorgia. The model is trained for topics like "პოლიტიკა და ომი", "სპორტი", "ეკონომიკა", "კოვიდ-19" with 85% of Accuracy.

you can go to the link and see our topic analyser open source