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Topic modeling applied on innovation studies of
Flemish companies
Master Thesis in Data Science
Statistics Flanders Data Science Event
May 24th, 2022Victor Vanhullebusch & Anne lien CrijnsPromotor:
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How to fill out topic modeling applied on
How to fill out topic modeling applied on
01
Gather a large dataset of text documents that you want to analyze.
02
Preprocess the text data by removing stop words, punctuation, and performing stemming or lemmatization.
03
Create a document-term matrix to represent the frequency of words in each document.
04
Choose the number of topics you want to extract from the text data.
05
Apply a topic modeling algorithm such as Latent Dirichlet Allocation (LDA) or Non-Negative Matrix Factorization (NMF) to the document-term matrix.
06
Interpret the results by examining the top words associated with each topic and the distribution of topics in each document.
Who needs topic modeling applied on?
01
Researchers in the field of natural language processing who want to discover themes or patterns in large collections of text data.
02
Businesses looking to analyze customer reviews, feedback, or social media posts to understand common themes or sentiments.
03
Content creators who want to organize and categorize large amounts of text content for better navigation and searchability.
04
Academics and educators who want to explore trends or topics in academic literature or student essays.
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What is topic modeling applied on?
Topic modeling is applied on a collection of text documents to discover the underlying topics within the corpus.
Who is required to file topic modeling applied on?
Researcher or organizations conducting text analysis or working with large text datasets may require to utilize topic modeling.
How to fill out topic modeling applied on?
Topic modeling can be done using algorithms such as Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF) on text data. The output topics can then be interpreted and analyzed.
What is the purpose of topic modeling applied on?
The purpose of topic modeling is to automatically identify the hidden patterns and themes present in large text datasets, making it easier to navigate and extract insights from the data.
What information must be reported on topic modeling applied on?
The identified topics, distribution of topics, and key terms in each topic must be reported when applying topic modeling.
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