Real-Time Stream Data Analytics for Multi-purpose Social Media Applications

2015 ◽  
Author(s):  
Satyen Abrol ◽  
Gunasekar Rajasekar ◽  
Latifur Khan ◽  
Vaibhav Khadilkar ◽  
Siddarth Nagarajan ◽  
...  
Keyword(s):  
Social Media ◽  
Real Time ◽  
Data Analytics ◽  
Stream Data ◽  
Media Applications

scholarly journals Real-time tracking and mining of users’ actions over social media

2020 ◽  
Vol 17 (2) ◽  
pp. 403-426
Author(s):  
Ejub Kajan ◽  
Noura Faci ◽  
Zakaria Maamar ◽  
Mohamed Sellami ◽  
Emir Ugljanin ◽  
...  
Keyword(s):  
Social Media ◽  
Web 2.0 ◽  
Sentiment Analysis ◽  
Real Time ◽  
Data Analytics ◽  
Web 2.0 Technologies ◽  
Media Companies ◽  
Real Time Tracking

With the advent of Web 2.0 technologies and social media, companies are actively looking for ways to know and understand what users think and say about their products and services. Indeed, it has become the practice that users go online using social media like Facebook to raise concerns, make comments, and share recommendations. All these actions can be tracked in real-time and then mined using advanced techniques like data analytics and sentiment analysis. This paper discusses such tracking and mining through a system called Social Miner that allows companies to make decisions about what, when, and how to respond to users? actions over social media. Questions that Social Miner allows to answer include what actions were frequently executed and why certain actions were executed more than others.

Emerging Trends and Techniques in Cloud-Based Data Analytics

2021 ◽  
pp. 37-47
Author(s):  
Shaila S. G. ◽  
Monish L. ◽  
Lavanya S. ◽  
Sowmya H. D. ◽  
Divya K.
Keyword(s):  
Social Media ◽  
Cloud Computing ◽  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Huge Data ◽  
Emerging Trends ◽  
Media Applications

The new trending technologies such as big data and cloud computing are in line with social media applications due to their fast growth and usage. The big data characteristic makes data management challenging. The term big data refers to an immense collection of both organised and unorganised data from various sources, and nowadays, cloud computing supports in storing and processing such a huge data. Analytics are done on huge data that helps decision makers to take decisions. However, merging two conflicting design principles brings a challenge, but it has its own advantage in business and various fields. Big data analytics in the cloud places rigorous demands on networks, storage, and servers. The chapter discusses the importance of cloud platform for big data, importance of analytics in cloud and gives detail insight about the trends and techniques adopted for cloud analytics.

Big Data Analytics for Multipurpose Social Media Applications

2017 ◽  
pp. 289-306
Author(s):  
Bhavani Thuraisingham ◽  
Mohammad Mehedy Masud ◽  
Pallabi Parveen ◽  
Latifur Khan
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Media Applications

Redis-Based Messaging Queue and Cache-Enabled Parallel Processing Social Media Analytics Framework

2020 ◽  
Author(s):  
Ravindra Kumar Singh ◽  
Harsh Kumar Verma
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Real Time ◽  
Data Analytics ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Social Media Analytics ◽  
Data Engineering ◽  
Extreme Gradient Boosting

Abstract The extensive usage of social media polarity analysis claims the need for real-time analytics and runtime outcomes on dashboards. In data analytics, only 30% of the time is consumed in modeling and evaluation stages and 70% is consumed in data engineering tasks. There are lots of machine learning algorithms to achieve a desirable outcome in prediction points of view, but they lack in handling data and their transformation so-called data engineering tasks, and reducing its time remained still challenging. The contribution of this research paper is to encounter the mentioned challenges by presenting a parallelly, scalable, effective, responsive and fault-tolerant framework to perform end-to-end data analytics tasks in real-time and batch-processing manner. An experimental analysis on Twitter posts supported the claims and signifies the benefits of parallelism of data processing units. This research has highlighted the importance of processing mentioned URLs and embedded images along with post content to boost the prediction efficiency. Furthermore, this research additionally provided a comparison of naive Bayes, support vector machines, extreme gradient boosting and long short-term memory (LSTM) machine learning techniques for sentiment analysis on Twitter posts and concluded LSTM as the most effective technique in this regard.

scholarly journals Massive Data Analytics for Macroeconomic Nowcasting

2021 ◽  
pp. 145-167
Author(s):  
Peng Cheng ◽  
Laurent Ferrara ◽  
Alice Froidevaux ◽  
Thanh-Long Huynh
Keyword(s):  
Social Media ◽  
Real Time ◽  
Data Analytics ◽  
Reliable Information ◽  
Empirical Literature ◽  
The Internet ◽  
Massive Data ◽  
Policy Makers ◽  
Recent Advances ◽  
Large Databases

AbstractNowcasting macroeconomic aggregates have proved extremely useful for policy-makers or financial investors, in order to get real-time, reliable information to monitor a given economy or sector. Recently, we have witnessed the arrival of new large databases of alternative data, stemming from the Internet, social media, satellites, fixed sensors, or texts. By correctly accounting for those data, especially by using appropriate statistical and econometric approaches, the empirical literature has shown evidence of some gain in nowcasting ability. In this chapter, we propose to review recent advances of the literature on the topic, and we put forward innovative alternative indicators to monitor the Chinese and US economies.

Professional and Business Applications of Social Media Platforms

2017 ◽  
pp. 427-450 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Social Media ◽  
Real Time ◽  
Business Performance ◽  
Digital Age ◽  
Business Applications ◽  
The World ◽  
Social Media Platforms ◽  
Strategic Goals ◽  
Use Of Social Media ◽  
Media Applications

This chapter provides an overview of the challenges and benefits of social media across various industries. The use of social media has created the highly effective communication platforms where any user, virtually anywhere in the world, can freely create the content and disseminate this information in real time to a global audience. The chapter argues that professional and business applications of social media platforms can enhance business performance toward reaching strategic goals in the digital age. What are keeping various industries awake these days? Why are social media applications important to various industries? How do social media platforms apply for professional and business perspectives across various industries?

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