How does topic modeling work using AI?
Topic modeling is an AI technique used to organize, understand, and summarize large collections of textual information. Topic modeling methods are used to scan text data and finds groups of words and phrases that best represent the topic of that text. This text can be in the form of customer service text chats, news snippets, large text documentation, etc. Using Skyl.ai AI Platform for NLP you can quickly build and deploy a high quality Topic modeling ML model in hours.
Topic modeling Industry use cases:
Financial Text Modeling
Research in ﬁnance and banking has started to utilize large amounts of textual data available through financial reports, regulatory ﬁlings, finance message boards, etc. This unstructured text data is analyzed to gain risk insight, predicting market variables, and measuring the financial conditions of entities. Topic modeling assists financial analysts to filter this text data, organizing and summarizing them so they can access information most relevant to their research.
Open-Ended Survey Analysis
Open-ended (OE) responses in surveys are widely used in market research studies. However, the analysis of OE response is associated with a huge workload. To identify the topics mentioned and their relative importance, analysts read and categorize all or a selection of responses manually. Such a manual process is time-consuming and prone to errors, especially when multiple researchers analyze the responses separately. Using topic modeling, AI systems can automatically identify the underlying concepts that are discussed within a collection of responses and determine which topics are being addressed.
IT Support Ticket Modeling
In IT support, hundreds, or even thousands of customer support tickets are raised every day. These tickets can be diverse and address a variety of issues across various IT support departments. Manually analyzing these incident tickets and routing them to the right team in a timely manner is not an easy task. NLP techniques can automate this task using topic modeling, understanding the ticket topic and context, to route the issue into the right category and right team. This reduces the time taken to resolve the issue and increases customer satisfaction.
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