Insight into Text Mining using ‘R’
#TextMining is the vastly used technique since a decade to gain information from the data we generate through various channels. As of now #TextAnalysis is hot new trend in #Analytics. So performing a text analysis will allow you to have insight of search patterns, reviews on your product in a quantifiable manner. #StatisticalAnalysis & #MachineLearning are one of the key methods used for text mining, as it has become huge and mainly an untapped source of data.
In general text mining is interdisciplinary field of activity amongst data mining, linguistics, computational statistics and computer science. The standard techniques used in text mining are text classification, text clustering, ontology & taxonomy creation, document summarisation & latent corpus analysis. In addition a lot of ETL techniques are being commonly used. The data mining communities like document clustering and document classification brought us classical applications in text mining. These communities brought us the idea to transform the text into a structured format based on “term frequencies” and subsequently apply ETL techniques.
To get to know on how Text Mining is being done using R, lets know how R came into picture of ETL tools and stayed as one of the best. From the past few years more innovative text mining methods have been used for analysis in various fields like linguistic stylometry. Linguistic stylometry states that the probability of a specific author wrote a specific text is calculated by analysing the authors writing style, or in search engines for learning rankings of documents from search engine logs of user behaviour. Recent technology updates brought developments in document exchange with valuable concepts for automatic handling of texts. The semantic web propagates standardised formats for document exchange to enable agents to perform semantic operations on them. This is implemented by providing metadata and annotating the text with tags. One of the key format is RDF, where efforts to handle this format have been made in R.
Now a days almost every major statistical computing product offers text mining capabilities and many well-known data mining products provide solutions for text mining tasks. The capabilities and features include preprocessing, association, clustering, summarising, categorising and usage of API’s.
A text mining analysis involves several challenging processes and an analysts tasks typically start with a set of heterogenous input texts. So the first step is to import these texts into one’s favourite computing environment in our case R. The two aspects of text mining using R are subject extraction and sentiment mining.
Watch the webinar on YouTube : Insight into Text Mining using R