Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources." Written resources may include websites, books, emails, reviews, and articles. High-quality information is typically obtained by devising patterns and trends by means such as statistical pattern learning. According to Hotho et al. (2005) we can distinguish between three different perspectives of text mining: information extraction, data mining, and a knowledge discovery in databases (KDD) process. Text mining usually involves the process of structuring the input text, deriving patterns within the structured data, and finally evaluation and interpretation of the output. 'High quality' in text mining usually refers to some combination of relevance, novelty, and interest. Typical text mining tasks include text categorization, text clustering, concept/entity extraction, production of granular taxonomies, sentiment analysis, document summarization, and entity relation modeling.
EXALEAD is a software company, created in 2000, that provided search platforms and search-based applications (SBA) for consumer and business users. The company was headquartered in Paris, France, and was a subsidiary of Dassault Systèmes.
Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, also more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly.
Social search is a behavior of retrieving and searching on a social searching engine that mainly searches user-generated content such as news, videos and images related search queries on social media like Facebook, LinkedIn, Twitter, Instagram and Flickr. It is an enhanced version of web search that combines traditional algorithms. The idea behind social search is that instead of ranking search results purely based on semantic relevance between a query and the results, a social search system also takes into account social relationships between the results and the searcher. The social relationships could be in various forms. For example, in LinkedIn people search engine, the social relationships include social connections between searcher and each result, whether or not they are in the same industries, work for the same companies, belong the same social groups, and go the same schools, etc.
Social media measurement, also called social media controlling, is the management practice of evaluating successful social media communications of brands, companies, or other organizations.
Amit Sheth is a computer scientist at University of South Carolina in Columbia, South Carolina. He is the founding Director of the Artificial Intelligence Institute, and a Professor of Computer Science and Engineering. From 2007 to June 2019, he was the Lexis Nexis Ohio Eminent Scholar, director of the Ohio Center of Excellence in Knowledge-enabled Computing, and a Professor of Computer Science at Wright State University. Sheth's work has been cited by over 48,800 publications. He has an h-index of 117, which puts him among the top 100 computer scientists with the highest h-index. Prior to founding the Kno.e.sis Center, he served as the director of the Large Scale Distributed Information Systems Lab at the University of Georgia in Athens, Georgia.
Marketing buzz or simply buzz—a term used in viral marketing—is the interaction of consumers and users with a product or service which amplifies or alters the original marketing message. This emotion, energy, excitement, or anticipation about a product or service can be positive or negative. Buzz can be generated by intentional marketing activities by the brand owner or it can be the result of an independent event that enters public awareness through social or traditional media such as newspapers. Marketing buzz originally referred to oral communication but in the age of Web 2.0, social media such as Facebook, Twitter, Instagram and YouTube are now the dominant communication channels for marketing buzz.
A hashtag is a metadata tag that is prefaced by the hash symbol, #. On social media, hashtags are used on microblogging and photo-sharing services such as Twitter or Tumblr as a form of user-generated tagging that enables cross-referencing of content by topic or theme. For example, a search within Instagram for the hashtag #bluesky returns all posts that have been tagged with that term. After the initial hash symbol, a hashtag may include letters, numerals, or underscores.
Search-based application is an software applications in which a search engine platform is used as the core infrastructure for information access and reporting. Search-based applications use semantic technologies to aggregate, normalize and classify unstructured, semi-structured and/or structured content across multiple repositories, and employ natural language technologies for accessing the aggregated information.
Yebol was a vertical "decision" search engine that had developed a knowledge-based, semantic search platform. Based in San Jose, California, Yebol's artificial intelligence human intelligence-infused algorithms automatically cluster and categorize search results, web sites, pages and contents that it presents in a visually indexed format that is more aligned with initial human intent. Yebol used association, ranking and clustering algorithms to analyze related keywords or web pages. Yebol presented as one of its goals the creation of a unique "homepage look" for every possible search term.
The Ubiquitous Knowledge Processing Lab is a research lab at the Department of Computer Science at the Technische Universität Darmstadt. It was founded in 2006 by Iryna Gurevych.
The social data revolution is the shift in human communication patterns towards increased personal information sharing and its related implications, made possible by the rise of social networks in the early 2000s. This phenomenon has resulted in the accumulation of unprecedented amounts of public data.
idio Ltd. is an enterprise software company that produces and implements products for brands and publishers. To do so, idio uses its cloud-hosted platform, which incorporates modules for large-scale content aggregation and structuring, content analytics, multi-channel marketing automation, and customer insight generation. idio has offices in London and Exeter in the UK.
The fields of marketing and artificial intelligence converge in systems which assist in areas such as market forecasting, and automation of processes and decision making, along with increased efficiency of tasks which would usually be performed by humans. The science behind these systems can be explained through neural networks and expert systems, computer programs that process input and provide valuable output for marketers.
Viralheat was a subscription-based software service for social media management that helps clients monitor and analyze consumer-created content. It was first released in beta in May 2009. Viralheat raised $75,000 in seed capital in December 2009 and $4.25 million of venture capital from the Mayfield Fund in 2011.
Topsy Labs was a social search and analytics company based in San Francisco, California. The company was a certified Twitter partner and maintained a comprehensive index of tweets, numbering in the hundreds of billions, dating back to Twitter's inception in 2006.
NetOwl is a suite of multilingual text and identity analytics products that analyze big data in the form of text data – reports, web, social media, etc. – as well as structured entity data about people, organizations, places, and things.
Social media mining is the process of obtaining data from user-generated content on social media in order to extract actionable patterns, form conclusions about users, and act upon the information. Mining supports targeting advertising to users or academic research. The term is an analogy to the process of mining for minerals. Mining companies sift through raw ore to find the valuable minerals; likewise, social media mining sifts through social media data in order to discern patterns and trends about matters such as social media usage, online behaviour, content sharing, connections between individuals, buying behaviour. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as such organizations can use the analyses for tasks such as design strategies, introduce programs, products, processes or services.
Semantic queries allow for queries and analytics of associative and contextual nature. Semantic queries enable the retrieval of both explicitly and implicitly derived information based on syntactic, semantic and structural information contained in data. They are designed to deliver precise results or to answer more fuzzy and wide open questions through pattern matching and digital reasoning.
