Given a dataset of documents, LDA backtracks and tries to figure out what topics would create those documents in the first place. Those topics then generate words based on their probability distribution. LDA assumes documents are produced from a mixture of topics. Latent Dirichlet Allocation is the most popular topic modeling technique and in this article, we will discuss the same. NonNegative Matrix Factorization techniques. There are many approaches for obtaining topics from a text such as – Term Frequency and Inverse Document Frequency.
Latent Dirichlet Allocation for Topic Modeling Tips to improve results of topic modelling.Latent Dirichlet Allocation for Topic Modeling.So, if you aren’t sure about the complete process of topic modeling, this guide would introduce you to various concepts followed by its implementation in python. They are being used to organize large datasets of emails, customer reviews, and user social media profiles. Various professionals are using topic models for recruitment industries where they aim to extract latent features of job descriptions and map them to right candidates. For Example – New York Times are using topic models to boost their user – article recommendation engines. Topic Models are very useful for the purpose for document clustering, organizing large blocks of textual data, information retrieval from unstructured text and feature selection. A good topic model should result in – “health”, “doctor”, “patient”, “hospital” for a topic – Healthcare, and “farm”, “crops”, “wheat” for a topic – “Farming”. Topics can be defined as “a repeating pattern of co-occurring terms in a corpus”. It is an unsupervised approach used for finding and observing the bunch of words (called “topics”) in large clusters of texts. Topic Modelling is different from rule-based text mining approaches that use regular expressions or dictionary based keyword searching techniques. As the name suggests, it is a process to automatically identify topics present in a text object and to derive hidden patterns exhibited by a text corpus.
One such technique in the field of text mining is Topic Modelling. But, technology has developed some powerful methods which can be used to mine through the data and fetch the information that we are looking for. With the growing amount of data in recent years, that too mostly unstructured, it’s difficult to obtain the relevant and desired information. Analytics Industry is all about obtaining the “Information” from the data.