A U T H O R S : G a n e s h S r i n i v a s a n a n d S a n d e e p W a g h Social Media Analytics

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1 contents A U T H O R S : G a n e s h S r i n i v a s a n a n d S a n d e e p W a g h Social Media Analytics Abstract... 2 Need of Social Content Analytics... 3 Social Media Content Analytics... 4 Inferences from Social Analytics... 5 Real-Time Analytics... 6 Building Knowledge... 7 LISA Assembly... 7 Conclusion... 9 References About the author(s) About L&T Infotech Social Media Analytics Page 1 of 12

2 Abstract Sites such as Facebook, Twitter, YouTube and Google are sources of social impressions. These sites form biggest platform for social networking and content sharing. Users generate huge amounts of content on these sites and yet, the content generated from these websites remains largely untapped. The content in the social sites can be very valuable especially in understanding the social sentiments and intent. The social media greatly impacts brand and its reputation. The analytics of this social content is being leveraged largely in the consumer driven industry (B2C). Social media analytics (SMA) is about harnessing the social data and transforming it into information that can enable an organization to improve brand visibility and increase top line sales. They can identify trends and patterns which can be used in the decision making process. Social Media Analytics Page 2 of 12

3 Need of Social Content Analytics Social media adoption will continue to grow and so will the need to optimize business to effectively tap into this rich channel of information. Here are some facts to show the influence of social media: 1.5 billion social networking users globally 1.2 billion Facebook users worldwide More than two million websites have been integrated with Facebook 490 million unique YouTube users per month 190 million tweets per day 30 billion pieces of content is shared on Facebook every month Brands and organizations on Facebook receive 34,722 Likes every minute Twitter processes seven terabytes of data every day Facebook processes 10 terabytes of data every day YouTube users upload 48 hours of new video every minute of the day LISA (L&T Infotech Social Media Analytics) is a tool to download social impressions and analyze &visualize the social trends. Social Media Analytics Page 3 of 12

4 Social Media Content Analytics The interactions in most social media sites are in textual format. Thus, text analysis is an important part of SMA. Elements Of Social Analytics Type of the text It helps us to precisely determine the nature of the text. For instance, in Facebook Fan page, posts are often created by the owner of the page. Thus, the nature of the text of the post will be informative or simply promotional. It does not make much sense in determining the sentiments from posts. However, other Facebook users might engage with a particular post by publishing their comments. These comments are valuable because it comes straight from the users and it can be thought as user s feedback for that post. Analyzing these comments for sentiments thus make great sense. Topic for the text It helps in categorizing the text so that we have to concentrate only on the relevant part ignoring the unwanted one. For instance, Suppose we have downloaded one day Twitter data for the keyword Justin to track Justin Beiber on Twitter and every second Justin is mentioned in nearly 30 tweets, in one day there are seconds which when multiplied with 30 gives 2,592,000 tweets/day. Now the possibility is that people might use only the word Justin to tweet about Justin Beiber or Justin Timberlake. Analyzing tweets intended for Justin Timberlake would add noise to the end result. Thus, we should filter the tweets based on the topic. In other words, tweets intended for Justin Timberlake must not be considered while tracking Justin Beiber on Twitter. Content for the text It helps us to determine the intent of text creation. A text based on its content can be sorted out into buckets of facts, guesses, opinions, beliefs, recommendations, queries, responses, feedbacks, etc. Same words can convey different meanings in different contexts. For instance, sentences given below have same words but they convey different meanings. Thus, sorting this content in appropriate bucket is essential. The movie is bad! (Fact) Is the movie bad? (Queries) The movie might be bad.(guess) Social Media Analytics Page 4 of 12

5 Inferences from Social Analytics 1. Sentiment Sentiment analysis is an application of natural language processing and other analytic techniques to identify and extract subjective information from the given text. Key aspects of this analysis include identifying the feature, aspect, or product about which a sentiment is being expressed, and determining the type, polarity (i.e., positive to negative ratio) and the degree and strength of the sentiment (i.e., subjectivity). Examples of applications include Exhibit 1: Sentiment Analysis companies applying sentiment analysis to analyze social media content to determine how consumer segments and stakeholders are reacting to their products and services. Data from social media, analyzed by natural language processing, can be combined with realtime sales data, in order to determine what effect a marketing campaign is having on customer sentiment and purchasing behavior. 2. Reach The main goal of an advertising campaign or a brand building campaign is to convey the intended message to a huge mass of users or followers. In the field of social media, the success of a campaign can be figured out by the depth of its reach. For instance: A Facebook fan page with more number of likes is likely to be more popular among its fans. A YouTube video with more views has reached a large number of audiences. Huge volume of tweets on a particular topic indicates that lot of people are buzzing about it. In other words, when there are more social impressions, there will be more brand visibility and eventually it will bring more revenue. Different publications or authors will have different influence through social media. Authors with more number of Twitter followers, Facebook friends and YouTube followers will reach a larger audience and create more brand visibility. Social Media Analytics Page 5 of 12

6 3. Trends Prediction is one of the potential elements of SMA. Analyzing the text can help us to find trends, correlations, and most importantly, make predictions. Predictive analysis is a set of techniques in which a mathematical model is created or chosen to best predict the probability of an outcome. Google analyzed the frequency of billions of flu symptoms related Web searches and demonstrated that it was possible to predict flu outbreaks with as much accuracy as the U.S. Centers for Disease Control and Prevention (CDC), whose predictions were based on a complex analytics applied to data painstakingly compiled Exhibit 2: Business Performance Analysis from clinics and physicians. Moreover, as people tend to conduct Internet research before visiting a doctor, the Web search data reveals, giving health care communities valuable lead time in preparing for outbreaks. Now, the CDC and other health organizations like the World Health Organization use Google Flu Trends as an additional disease monitoring tool. Real-Time Analytics Real-time analytics is a way to instantly view and track social impressions. This gives a realtime picture of social sentiments and consumer reactions. Real-time stream of social impressions can be monitored through LISA real-time analytics dashboard which provides the following insights: Total conversation Conversation rate Total reach of the conversation Subjectivity and polarity of the conversation Top keywords Sentiments Trend of conversation Tweets with information such as author, created date, content, followers, friends, language, location Option to view the Twitter profile of the user of the tweet and reply to that tweet instantly Social Media Analytics Page 6 of 12

7 Building Knowledge Organizations fine tune their strategies based on social impressions. The goal of social analytics is to harness knowledge from it. Raw text collected from various social media sites needs to be processed and the information can provide valuable insights. This is a continuous process of learning and re-learning from the historical data and identifying new trends from social impressions. LISA Assembly LISA operates as a four-stage assembly as described in Exhibit 3 Exhibit 3: LISA framework Stage 1: Downloading social impressions from various sources This is a continuous process which runs in batch mode. Scheduler runs the job of fetching raw data from various social media sites like Twitter, Facebook, YouTube and Google in different formats and from different sources and is downloaded on a periodic basis. Stage 2: Storing data in Big Data repository It is a clustered approach to store the data on multiple nodes. Data replication and balancing of data storage is taken care by the Hadoop framework. The Hadoop framework provides speed, reliability and data motion to applications. Stage 3: Processing LISA uses the Map Reduce framework, where the application is divided into many small fragments of work, each of which may be executed or re-executed on any node in the cluster. In addition, it provides a distributed file system that stores data on the compute nodes, providing very high aggregate bandwidth across the cluster. It enables applications to work with thousands of computation-independent computers and petabytes of data. Social Media Analytics Page 7 of 12

8 Stage 4: Reporting and Visualization LISA stores processed output in MongoDB which is an object database storing information in the form of XML or JASON format. The information in this database can be presented to users through charts. Insights include details such as: Sentiment and lifecycle analysis Brand comparison Gender analysis Business performance correlation Demography analysis Reach analysis Predictive analysis Keyword trend analysis Competitive analysis Social impression Business performance to social impression correlation Exhibit 4 : LISA Social Analytics Dashboard Social Media Analytics Page 8 of 12

9 Conclusion With SMA, monitoring the flow of social impressions is now a business necessity. Keeping an eye on the pulse of public opinion is now an integral part of various departments in an enterprise such as R&D, Operations, HR, Sales, Marketing and Executive Board. Statistically derived intelligence from analytics tools like LISA will serve as an advantage for those who want to harness social analytics for their competitive advantage. Social Media Analytics Page 9 of 12

10 Abbreviations and Acronyms LISA L&T Infotech Social Analytics SMA B2C Social Media Analytics Business to Consumers References Map-reduce algorithmic processing Google monitoring flu trends Social networking statistical information Social Media Analytics Page 10 of 12

11 About the author(s) Passport size snap of the author Bangalore. Passport size snap of the About L&T Infotech author Ganesh Srinivasan is a Delivery Head at L&T Infotech. He has more than 20 years of experience in various software development disciplines such as programming, software architecting and program and delivery management. He has 10 years of international experience and has successfully managed large IT projects executed with the onsite/offshore model. Ganesh is a graduate in engineering from Barathiar University. He has completed Executive Advanced Management program from IIM Sandeep Wagh is a Lead Architect at L&T Infotech. He has more than eight years of experience in various software development disciplines such as programming, software architecting and strategic program management. He has four years of international experience and has successfully managed large IT projects executed with the onsite/offshore model. Sandeep is a graduate in engineering from the University of Mumbai. He is a Sun Java Certified Architect. He is also a TOGAF certified Enterprise Architect. L&T Infotech is a wholly-owned subsidiary of the Multi-billion USD Larsen & Toubro (ranked 9th by Forbes International among the World s Most Innovative Companies and ranked 4th in the global list of Green Companies in the industrial sector by the Newsweek magazine.) which has a presence in construction, engineering, manufacturing and financial services. One of the fastest growing IT Services companies, L&T Infotech is ranked by NASSCOM as the 8th largest software & services exporter from India and among the top 20 IT BPO employers. L&T Infotech provides end-to-end solutions and services in the following verticals: Banking & Financial Services; Insurance; Travel & Logistics, Media & Entertainment, Healthcare, Manufacturing, Energy & Process, Utilities, E&C, Hi-tech & Consumer Electronics, Product Engineering Services (PES), Consumer Packaged Goods, Retail & Pharmaceuticals, Auto & Aerospace, and Industrial Products. L&T Infotech also delivers business solutions to its clients in the following horizontals/service lines: Testing, Mobility, Infrastructure Management System; Business Intelligence/Data Warehousing, SAP, Oracle and Microsoft, Enterprise Integration and Manufacturing Execution Systems, in addition to an innovative CIOthought partnership program that provides a value-driven edge to clients. L&T Infotech s horizon is filled with the promise of new and cutting edge offerings in the technology space including an end-to-end cloud computing adoption toolkit and cloud advisory consulting services; enterprise mobility solutions covering a smart access platform; big data advisory services; and in-memory computing. L&T Infotech has developed intellectual properties (IPs) in all the vertical and horizontal service lines and leverages them to provide IP-led solutions. Social Media Analytics Page 11 of 12

12 L&T Infotech, headquartered in Mumbai, India, has a global presence across continents. The Company prides itself on a culture of training and mentoring. Coupled with a work ethos that encourages innovation, the Company lists high among the best companies to work for. L&T Infotech is differentiated by its unique Business-to-IT Connect which emerges out of its rich corporate heritage. ( For more information, visit us at or us at info@lntinfotech.com Follow L&T Infotech on: *All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from L&T Infotech Financial Services Technologies Inc. Social Media Analytics Page 12 of 12