Basics About Topic Modelling As A Data Analytics TechniqueNovember 24, 2016
The Data Science industry has brought about various new avenues into the world of business and internet of things. Here, data analytics as a field, basically deals with extracting ‘information’ from all the obtained data. With rapid digitalization and increasing of the boundaries of the virtual world, the generation and availability of data is on an all-time high. While some of this data might be pre-processed and structured, most of it is just not structured at all. This causes a lot of difficulties when it comes to the part, where relevant and important information is required. That’s where the tools and technologies of the data analytics industry come into play. These are powerful methods, developed by technology and can be used for sifting through the volumes of data and sniffing out, exactly what a professional is looking for. One of the subsets of these technology is the field of text mining, which basically deals with the technique known as Topic Modelling.
This process mainly deals with, identifying topics present in a text object and deriving hidden patterns automatically, thus aiding in the betterment of decision making. This process differs from other run of the mill text mining approaches, which basically deal with regular search techniques or keywords searching techniques based on any random dictionary. A specific bunch of words that is supposed to be found and observed by a professional, is known as “topics”, which usually are present in large clusters of texts. Topic modelling is the unsupervised approach to performing the above mentioned action, with only the machine and no manual help.
Topics in other words are, “a pattern of co-occuring terms in a corpus, which keeps repeating itself”. For instance while building a topic model for healthcare, it should be devised in such a way that it results in words like, health, doctor, patient, hospital and other related words. These topic models are very useful when it comes to processes such as, document clustering, organizing large blocks of textual data, feature selection and retrieval of information from unstructured text and so on. What makes this technique so very important is that it can be used in almost any field from print media to marketing and still be relevant and product centric. For example, there are top gun newspaper publishing houses like, The New York Times, who have a team working on perfecting topic models so as to boost their article recommendations for users. There are a lot of advanced HR teams dabbling in this sector by trying to use it to match perfect candidates, with perfect job profiles
These text models are also used in various other applications such as organization of large datasets of emails, customer reviews and user social media profiles. These are some of the reasons why professionals specializing in this technique are gradually becoming sought after. As the demand of companies rises, the amount of people opting to get trained in these techniques also goes up. Imarticus Learning has various industry intensive course offerings for various data analytics tools like Python, which uses this topic modeling technique most extensively.
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