'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. It is obvious this topic focuses on “BerTopic allows you to visualize the topics that were generated in a way very similar to LDAvis. This will allow you to get more insights into the topic's quality. 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.In the above graph, you can see Top words in Topic 4 are proud, thank, cheer4india, cheering, and congrats.You can also visualize how similar certain topics are to each other. To visualize the heatmap, simply call.
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