Only the slides are enough. I can add the voice overlay.
SIT717 Enterprise Business Intelligence Trimester 2 – 2019 Assignment 2 – Data Analytic Technical Report Title: The Integration of Business Intelligence and Data Mining in Short Text Mining Name: Krithika Arulselvam Student Id: 218019529 Assignment 2 - Data Analytic Technical Report 1 Table of Contents Text/short text mining ............................................................................................................................. 1 An introduction of a data analytic application background, motivation and aim ...................................... 2 Examples of Text Mining:......................................................................................................................... 3 Motivation: ............................................................................................................................................. 4 A summary of our data set: ..................................................................................................................... 4 Limitations: ............................................................................................................................................. 8 Evaluation and Demonstration .............................................................................................................. 10 Conclusion:............................................................................................................................................ 11 References:……………………………………………………………………………………………………………………………………………13 Assignment 2 - Data Analytic Technical Report 2 Text/short text mining Meaning of text mining can be understood first before starting discussion on the topic. As the name itself suggests, text mining refers to the analytics of text. As mining is meant for exploiting the valuables from deep earth, text mining is the process of deriving very high quality information from text. The objective of text mining is to turn plain text into data for information, analysis, scrutiny, and this is done with the help of natural language processing known as NLP and analytical methods. Thus text mining includes classification of data, clustering of data, and extraction of concept from the text by technical methods, sentiment analysis, etc. Cognitive devices are designed and built by the experts to divide the text converted data into granular partitions. By extract, we mean the ability to insert data into a database. Thus text mining is mainly used to extract only the relevant information we need from a lengthy and unwieldy data base. Thus this acts as a filter to give us the required data for our research ready made without much strain and time waste. An introduction of a data analytic application background, motivation and aim History: Data analytic applications started as early as in 1990’s. Initially, this was used only by business people to extract information about the market, consumers’ preferences, past trends and hence estimation of future demands and prices. As time elapsed, these process techniques were slowly started to apply to other fields also. The use of Data analytics was first used by Fredrick Winsaw Lawyer who used time management exercises, in the 19 th century itself. It was followed by Henry ford, in the 1960’s, when he used this to measure the speed of assembled lines. Because in census information or statistical bureau, we have big data comprising of so many data entries, this data analytic application to extract only data relevant for a particular year/country/race, etc was applied. Hence data analysis involves after collection of data by any Assignment 2 - Data Analytic Technical Report 3 source may be primary or secondary organize the data in a presentable manner may be by way of tables or bar charts or frequency distributions for any viewer to understand the raw data in an easy comprehensive manner. This also involves describing the data such as whether data has outliers, whether data is deviated much from the mean, whether data is skewed etc. Also next step is to analyze the data and interpret as perour requirement. The descriptive statistics include finding of mean, median, mode, range, Quartiles and outliers, standard deviations etc. The bell shaped curve shows symmetry otherwise we find that the data is skewed either to right or left. Examples of Text Mining: Text Mining is a tool which helps in getting the data cleaned up. Text mining techniques are basically cleaning up unstructured data to be available for text analytics In literature, say if we want to deeply study a particular aspect, then from the lengthy book with summary or meanings. Similarly research persons may be interested in a particular aspect say number of cancer deaths in a particular locality in a year. The mortality table will give you all information about the deaths in so many periods, for all causes. The researcher now can extract the particulars for the year first and then extract only deaths due to cancer. Thus now the data is ready for his research and other purposes. Instead, if he has to compile data individually from primary survey means it would be a very long, tiresome and unwieldy process. He has to send questionnaires to the relevant hospitals and get information in time. Thus data mining from already available information makes his work easier. Similarly from student point of view also, if a student is interested to learn pre calculus topic from internet, if he refers a full Math book, this will have algebra, geometry, calculus, trigonometry, etc. Now he has to extract only calculus topic and then filter pre calculus to learn. Thus data mining can also be termed as data filtering for our needs from time to time. Assignment 2 - Data Analytic Technical Report 4 Motivation: As already discussed this extraction of data in a shorter time from a reliable source motivates the researchers to concentrate on their study about the topic instead of wasting time about collection of data, and ensuring the reliability of data, etc. A summary of our data set: Now I am interested to start a real estate business, say. I want to