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Book Cover for: Machine Learning For Unstructured Data Modeling, Varadarajan B. L. Ramesh

Machine Learning For Unstructured Data Modeling

Varadarajan B. L. Ramesh

Content sharing sites are increasingly becoming a source of vital behavioral and attitudinal

information for many organizations. Corporations are realizing the economic value of the

information stored in such sites (Ghose and Panagiotos, 2010). Blog sites, online journals, wiki

pages and social media sites have tons of information created instantaneously at real time by the

users of these pages to express their varied opinions from the launch of latest tech-loaded

smartphones to political happenings and to soccer league results. On micro-blogging sites such as

Twitter, there are around 330 million monthly active users who create about 500 million tweets

per day (Twitter, 2017). An important aspect of the data residing on these social networks is that

it is completely crowdsourced - the people who are active on these platforms create this data

instantaneously in real time. Since these are tagged to real users and not anonymous, they give a

fair idea about people's opinion and attitudes towards a brand, firm or service. For corporations, it

also gives a way to assess drivers for future sales and business (Dhar and Chang, 2009). Initially,

social media had been associated with teens, but this is not the case anymore and people from

almost every age-group are expressing themselves on social media. About 75% of the internet

users use social media and this number is increasing day by the day (Kaplan and Haenlein, 2010).

Organizations have increasingly turned to Business Intelligence (BI) in the last decade to derive

useful insights and recommendations in their decision making. The organizational performance is

directly influenced by these systems (Ramakrishnan, Jones and Sidorova, 2012). In most

organizations, the BI systems have already added to businesses, thereby making them more

effective and fostering innovation (Watson and Wixom, 2007) and also helping them to respond

to changes in the business landscape very quickly and effectively (Gessner and Volonio, 2005).

However, these used only post facto data and they addressed only the historical aspects. Adding a

real-time component to these systems would greatly increase their efficiency and capabilities and

this leads to two generalized scenarios in the dynamic business environmen

Book Details

  • Publisher: Sahitya Nilayam Book Services
  • Publish Date: Apr 8th, 2023
  • Pages: 158
  • Language: English
  • Dimensions: 9.00in - 6.00in - 0.34in - 0.48lb
  • EAN: 9781805271512
  • Categories: Data Science - Machine Learning