'NLP Tutorial: Topic Modeling in Python with BerTopic' by Davis_McDavid hackernoontopstory topicmodeling
After training the model, you can access the size of topics in descending order.Topic -1 is the largest and it refers to outliers tweets that do not assign to any topics generated. In this case, we will ignore Topic -1.You can select a specific topic and get the top n words for that topic and their c-TF-IDF scores.For this selected topic, common words are Sweden, goal,rolfo, swedes, goals, soccer.
In this article, we will look at three methods to visualize the topics.The visualize_topics method can help you visualize topics generated with their sizes and corresponding words. The visualization is inspired by LDavis.The visualize_barchart method will show the selected terms for a few topics by creating bar charts out of the c-TF-IDF scores. You can then compare topic representations to each other and gain more insights from the topic generated.
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