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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. 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 using quantitative techniques (e.g., computational statistics, machine learning and complexity) and so-called big data for data mining and simulation modeling. 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
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. The semantic representation of natural language (Bloomsbury studies in theoretical linguistics; Bloomsbury studies in theoretical linguistics). 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. 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. 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. 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.

Research challenges
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. 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. 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.

Social media analytics in Business Intelligence
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. 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.

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.

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.

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.

Analytical tools
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.

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. 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
 * Google Analytics
 * Mixpanel
 * Keen.io
 * Tableau