Post by juthi52943 on Dec 25, 2023 23:48:24 GMT -8
Latent Semantic Analysis (LSA). Unlike LDA models, LSA models are based solely on the frequency of words in text data, and do not take into account the probabilities of a subject generating specific words. It uses these frequencies to group a document with other documents containing a similar distribution of these words. Limitations of topic modeling Although topic modeling is a popular natural language technique.
Its drawbacks can limit its use cases. For Job Function Email List example : Short and long texts. While LDA and LSA models can work with short texts as well as long texts, other topic modeling methods face quality issues. the challenges of processing short texts . This reduces the accuracy of any analysis you perform, for example, on social media text Topics.
Topics generated by topic modeling will not be as accurate as topics produced by a supervised learning model such as topic classification, which means you often cannot use the results for finer-grained analysis. Number of subjects. Topic templates must be given the number of topics to search for.
Its drawbacks can limit its use cases. For Job Function Email List example : Short and long texts. While LDA and LSA models can work with short texts as well as long texts, other topic modeling methods face quality issues. the challenges of processing short texts . This reduces the accuracy of any analysis you perform, for example, on social media text Topics.
Topics generated by topic modeling will not be as accurate as topics produced by a supervised learning model such as topic classification, which means you often cannot use the results for finer-grained analysis. Number of subjects. Topic templates must be given the number of topics to search for.
