Tech Scouter

Hi readers, In the previous post, I wrote about gaining the knowledge from the Text which is available from many sources. In this post, I will be writing about Topic Mining. Introduction Topic Mining can be described as finding words from the group of words which can best describe the group. Textual Data in raw form is not associated with any context. A human can easily identify the context or topic for an article by reading the article and categorise it in one or other category like politics, sports, economics, crime etc. One of the factors any human will consider while classifying the text into one of the topics is the knowledge that how a word is associated with a topic e.g India won Over Sri Lanka in the test match . World Badminton Championships: When and where to watch Kidambi Srikanth ’s first round, live TV coverage, time in IST, live streaming   Here we may not find word sports explicitly in the sentences but the words marked in bold are associated

Hi Readers, Recently I was going through some text analytics activities at work and learned some techniques for text analytics and mining. In this series of posts, I will be sharing my experiences and learnings. Introduction Firstly the jargon Text Analysis and Text Mining are sub domains of the term Data Mining and are used interchangeably in most scenarios. Broadly  Text Analysis  refers to, Extracting information from textual data keeping the problem for which we want to get data in mind. and  Text Mining  refers to, the process of getting textual data. Nowadays, a large quantity of data is produced by humans.Data is growing faster than ever before and by the year 2020, about  1.7 megabytes of new information will be created every second for every human being on the planet and one of the main components of this data will be textual data. Some of the main sources of textual data are Blogs Articles Websites Facebook Comments Discussion Forums Reviews