Why topic modeling?

 


As large amounts of data are collected every day, more and more information becomes available. At the same time, it becomes difficult to access the necessary information that we are looking for.


Moreover, as these kinds of data are unstructured or free-form text, analyzing such volumes of text data manually becomes highly tedious and time-consuming.

The simple solution is to use Topic modeling, as it provides us with methods for automatically organizing, understanding, searching, and summarising extensive electronic archives.

It can help us sort through unstructured data in the following ways:


- Discovering the hidden themes in the collection.

- Classifying the documents into the discovered themes.

- Using the classification to organize/summarise/search the documents.


With the use of Topic Modeling, we can figure out what topics a bunch of unstructured text cover. This set of text documents may range from emails to survey responses, support tickets, product reviews, etc. Once we identify the topics, we can easily group them accordingly.

For example, a document belongs to the topics dogs, food, and health. And if a user queries for “dog food”, they might find this document relevant because it covers those topics (among others).


Therefore, we can figure its relevance for the query without even going through the whole document. By annotating the document based on the topics predicted by the modeling method, we can optimize our search process.


With the help of Textrics, you can organize, search and understand large quantities of information very quickly, no matter which platform they originate from.

Request a free demo right away. For more help, contatct our team of experts who can guide you every step of the way.


Further Reading → Know the objective of the text generated by user throguh Intent Analysis.


Location : United States    
Web : https://www.textrics.ai

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