Exploring Data Analytics of Data Variety

Abstract

The Internet allows organizations managers access to large amounts of data, and this data are presented in different formats, i.e., data variety, namely structured, semi-structured and unstructured. Based on the Internet, this data variety is partly derived from social networks, but not only, machines are also capable of sharing information among themselves, or even machines with people. The objective of this paper is to understand how to retrieve information from data analysis with data variety. An experiment was carried out, based on a dataset with two distinct data types, images and comments on cars. Techniques of data analysis were used, namely Natural Language Processing to identify patterns, and Sentimental and Emotional Analysis. The image recognition technique was used to associate a car model with a category. Next, OLAP cubes and their visualization through dashboards were created. This paper concludes that it is possible to extract a set of relevant information, namely identifying which cars people like more/less, among other information.

Sentimental classification analysis of polarity multi-view textual data using data mining techniques

“…Manually processing of this huge amount of unstructured textual data and information is very costly in term of time-consuming and expensive process. Accordingly, taking advantages of this type of data and information is very hard for the reason of its availability in various forms such as unstructured or semi-structured text data forms [2,3]. The majority of indexed resources of this textual data are mainly extracted from online reports and documents, e-mails, online textual blogs, memos, digital letters, etc [3].…”