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Social media is defined as web-based and mobile-based Internet applications that allow the creation, access and exchange of user-generated content that is ubiquitously accessible[1]. Besides social networking media (e.g., Twitter and Facebook), for convenience, we will also use the term ‘social media’ to encompass really simple syndication (RSS) feeds, blogs, wikis and news, all typically yielding unstructured text and accessible through the web. Social media is especially important for research into computational social science that investigates questions[2] using quantitative techniques (e.g., computational statistics, machine learning and complexity) and so-called big data for data mining and simulation modeling [3]. This has led to numerous data services, tools and analytics platforms. However, this easy availability of social media data for academic research may change significantly due to commercial pressures.

Terminology

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Some of the key techniques related to Social Media Analytics unstructured textual data: Natural language processing—(NLP) is a field of computer science, artificial intelligence and linguistics concerned with the interactions between computers and human (natural) languages [4]. The semantic representation of natural language (Bloomsbury studies in theoretical linguistics; Bloomsbury studies in theoretical linguistics) [5]. London: Bloomsbury Academic. Specifically, it is the process of a computer extracting meaningful information from natural language input and/or producing natural language output. News analytics—the measurement of the various qualitative and quantitative attributes of textual (unstructured data) news stories. Some of these attributes are: sentiment, relevance and novelty. Opinion mining—opinion mining (sentiment mining, opinion/sentiment extraction) is the area of research that attempts to make automatic systems to determine human opinion from text written in natural language [6]. Scraping—collecting online data from social media and other Web sites in the form of unstructured text and also known as site scraping, web harvesting and web data extraction [7]. Sentiment analysis—sentiment analysis refers to the application of natural language processing, computational linguistics and text analytics to identify and extract subjective information in source materials [8]. Text analytics—involves information retrieval (IR), lexical analysis to study word frequency distributions, pattern recognition, tagging/annotation, information extraction, data mining techniques including link and association analysis, visualization and predictive analytics [9].

Research challenges

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Social media scraping and analytics provides a rich source of academic research challenges for social scientists, computer scientists and funding bodies. Challenges include, Scraping—although social media data is accessible through APIs, due to the commercial value of the data, most of the major sources such as Facebook and Google are making it increasingly difficult for academics to obtain comprehensive access to their ‘raw’ data; very few social data sources provide affordable data offerings to academia and researchers. News services such as Thomson Reuters and Bloomberg typically charge a premium for access to their data. In contrast, Twitter has recently announced the Twitter Data Grants program, where researchers can apply to get access to Twitter’s public tweets and historical data in order to get insights from its massive set of data (Twitter has more than 500 million tweets a day). Data cleansing—cleaning unstructured textual data (e.g., normalizing text), especially high-frequency streamed real-time data, still presents numerous problems and research challenges. Holistic data sources—researchers are increasingly bringing together and combining novel data sources: social media data, real-time market & customer data and geospatial data for analysis. Data protection—once you have created a ‘big data’ resource, the data needs to be secured, ownership and IP issues resolved (i.e., storing scraped data is against most of the publishers’ terms of service), and users provided with different levels of access; otherwise, users may attempt to ‘suck’ all the valuable data from the database [10]. Data analytics—sophisticated analysis of social media data for opinion mining (e.g., sentiment analysis) still raises a myriad of challenges due to foreign languages, foreign words, slang, spelling errors and the natural evolving of language. Analytics dashboards—many social media platforms require users to write APIs to access feeds or program analytics models in a programming language, such as Java. While reasonable for computer scientists, these skills are typically beyond most (social science) researchers. Non-programming interfaces are required for giving what might be referred to as ‘deep’ access to ‘raw’ data, for example, configuring APIs, merging social media feeds, combining holistic sources and developing analytical models[11]. Data visualization—visual representation of data whereby information that has been abstracted in some schematic form with the goal of communicating information clearly and effectively through graphical means. Given the magnitude of the data involved, visualization is becoming increasingly important [12].

Social media analytics in Business Intelligence

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Business Intelligence (BI) can be described as "a set of techniques and tools for the acquisition and transformation of raw data into meaningful and useful information for business analysis purposes". The goal of BI is to allow for the easy interpretation of these large volumes of data. Identifying new opportunities and implementing an effective strategy based on insights can provide businesses with a competitive market advantage and long-term stability [13]. Business Intelligence is made possible through web-based reporting. With the introduction of web-based reporting, users around the world could share one centralized version of their data. All data and reports began to be stored in a single location, which made it easier to manage the information that was being dispersed within and outside an organization. A few key benefits of web-based reporting are,

  • Centralization of information Ease of management,
  • Maintenance,
  • Administration Minimization of IT overhead,
  • Improved data security. [14]

Sentiment Analyser is a technology framework in the field of Social Business Intelligence that leverages Informatica products. It is designed to reflect and suggest the focus shift of businesses from transactional data to behavioral analytics models. Sentiment Analyser frame work enables businesses to understand customer experience and ideates ways to enhance customer satisfaction[15].

Social media analytics is a nascent and emerging discipline that can help organizations formulate and implement measurement techniques for deriving insights from social media interactions and for evaluating the success of their own social media initiatives. Ultimately, a successful social media analytics program can enable businesses to improve their performance management initiatives across various business functions[16].

Buried within the mountains of social media chatter are nuggets of valuable data -- customer comments and opinions on companies, their products and services, breaking news and market trends. Every day, customers and prospective buyers offer feedback and engage in online conversations about businesses on sites like Facebook and Twitter. Organizations looking for a competitive edge can use social media monitoring and analytics tools to find, sort and analyze that data. Among other potential benefits, social media analytics offers businesses the ability to identify patterns in customer sentiment and gauge their marketing effectiveness[17].

Common Use-Cases for

Social Media Analytics

! Required

Business Insight

! Enabling Social

Media Analytics Techniques

! Pertinent Social Media

Performance Metrics

Social Media

Audience Segmentation

Which segments to

target for acquisition, growth or retention? Who are the advocates and influences for brand or product?

Social Network

Analysis|| Active Advocates Advocate Influence

Social Media

Information Discovery|| What are the new or emerging business relevant topics or themes? Are new communities of influence emerging?

Natural Language

Processing Complex Event Processing

Topic Trends

Sentiment Ratio

Social Media

Exposure & Impact

What are the brand

perceptions among constituents? How does brand compare against competitors? Which social media channels are being used for discussion?|| Social Network Analysis Natural Language Processing

Conversation Reach

Velocity Share of Voice Audience Engagement

Social Media

Behavior Inferences || What is the relationship among business relevant topics and issues? What are the causes for expressed intent (buy, churn etc.)?

Natural Language

Processing Clustering Data Mining

Interests or

Preferences (Theme) Correlations Topic Affinity Matrices

Analytical tools

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Enterprises are flooded in data about their customers, prospects, internal business processes, suppliers, partners and competitors. Often, they can't leverage this flood of data and convert it to actionable information for growing revenue, increasing profitability and efficiently operating the business. Business intelligence (BI) tools are the technology that enables business people to transform data into information that will help their business [18].

Although Business Intelligence BI tools have been around for decades and many consider the industry mature, the BI market is vibrant, constantly innovating and evolving to meet the ever-expanding needs of businesses of all sizes and industries. Over the years, many BI tool styles have emerged to match the varied ways that business people need to analyze data[19]. An understanding of BI tool categories and styles is needed in order to match your analytical needs with the appropriate tools. Some of the most commonly used Analytical tools are

References

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  1. ^ Kaplan AM, Haenlein M (2010) Users of the world, unite! the challenges and opportunities of social media. Bus Horiz 53(1):59–68 2.
  2. ^ Lazer D et al (2009) Computational social science. Science 323:721–723
  3. ^ Cioffi-Revilla C (2010) Computational social science. Wiley Interdiscip Rev Comput Statistics 2(3):259–271
  4. ^ Jackson, P., & Moulinier, I. (2002). Natural language processing for online applications : Text retrieval, extraction, and categorization (Natural language processing, v. 5; Natural language processing (Amsterdam, Netherlands), v. 5). Amsterdam: John Benjamins Pub.. http://site.ebrary.com/id/10022351
  5. ^ Szumska, D. (2013). The adjective as an adjunctive predicative expression : A semantic analysis of nominalised propositional structures as secondary predicative syntagmas (Studien zur Text- und Diskursforschung, Band 2; Studien zur Text- und Diskursforschung, Band 2). Frankfurt am Main: Peter Lang Edition. http://public.eblib.com/choice/publicfullrecord.aspx?p=1564684
  6. ^ Zhou, X., Wan, X., & Xiao, J. (2016). CMiner: Opinion Extraction and Summarization for Chinese Microblogs. IEEE Transactions On Knowledge And Data Engineering, 28(7). doi:10.1109/TKDE.2016.2541148
  7. ^ Munzert, S., Ruoba, C., Meiboner, P., & Nyhuis, D. (2015). Automated data collection with R : A practical guide to Web scraping and text mining. Chichester, England: Wiley. http://catalogimages.wiley.com/images/db/jimages/9781118834817.jpg
  8. ^ Danneman, N., Heimann, R., & Wood, M. G. (2014). Social media mining with R : Deploy cutting-edge sentiment analysis techniques to real-world social media data using R (Community Experience Distilled; Community experience distilled). Birmingham, England: Packt Publishing. http://site.ebrary.com/id/10854991
  9. ^ Mason, M., & Lee, H. J. S. (2012). Reading colonial Japan : Text, context, and critique. Stanford, Calif.: Stanford University Press. http://site.ebrary.com/id/10537875
  10. ^ Roldán, M. C. (2010). Pentaho 3.2 data integration : Beginner's guide : explore, transform, validate, and integrate your data with ease. Birmingham, U.K.: Packt Pub.. http://site.ebrary.com/id/10439364
  11. ^ Search Business Analytics, Tech Target. "Social Media Analytics". techtarget.com. Retrieved 25 February 2015.
  12. ^ Azzam, T., & Evergreen, S. D. H. (2013). Data visualization. Part 2 / (New directions for evaluation, number 140 (Winter 2013); New directions for evaluation, no. 140). San Francisco: A publication of Jossey-Bass and the American Evaluation Association. http://public.eblib.com/choice/publicfullrecord.aspx?p=1579356
  13. ^ Adkison, D. (2013). IBM Cognos business intelligence : Discover the practical approach to BI with IBM Cognos business intelligence. Birmingham England: Packt Publishing/Enterprise. http://site.ebrary.com/id/10701568
  14. ^ Odden, L. (2012). Optimize : How to attract and engage more customers by integrating SEO, social media, and content marketing. Hoboken, New Jersey: Wiley. http://www.123library.org/book_details/?id=52188
  15. ^ IT Glossary, Gartner. "Social Analytics - Gartner IT Glossary". www.gartner.com. Retrieved 25 February 2015.
  16. ^ Tera, Data. "Capitalize On Social Media With Big Data Analytics". www.forbes.com. Retrieved 27 May 2015.
  17. ^ Soleman, Ramzi; Cohard, Philippe (3 - 4 of March 2016). "Success Factors of Social Media Monitoring". ICTO 2016: Information and Communication Technologies in Organizations and Society. Retrieved 17 April 2016
  18. ^ Fasano, A., & Marmi, S. (2006). Analytical mechanics : An introduction (Oxford graduate texts; Oxford graduate texts). Oxford: Oxford University Press. http://public.eblib.com/choice/publicfullrecord.aspx?p=422398
  19. ^ Adkison, D. (2013). IBM Cognos business intelligence : Discover the practical approach to BI with IBM Cognos business intelligence. Birmingham England: Packt Publishing/Enterprise. http://site.ebrary.com/id/10701568