Semantic Interation, Text Mining, Tools and Technologies

Massive quantities of information continue accumulating at about 1.5 billion gigabytes per year in numerous repositories held at news agencies, at libraries, on corporate intranets, on personal computers, and on the Web. A large portion of all available information exists in the form of text. Researchers, analysts, editors, venture capitalists, lawyers, help desk specialists, and even students are faced with text analysis challenges. Text mining tools aim at discovering knowledge from textual databases by isolating key bits of information from large amounts of text, identifying relationships among documents. Text mining technology is used for plagiarism and authorship attribution, text summarization and retrieval, and deception detection.

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An Application of Text Mining to Capture and Analyze eWOM

Advances in Marketing, Customer Relationship Management, and E-Services - Capturing, Analyzing, and Managing Word-of-Mouth in the Digital Marketplace ◽

10.4018/978-1-4666-9449-1.ch010 ◽

2016 ◽

pp. 168-186 ◽

Cited By ~ 2

Author(s):

Taşkın Dirsehan

Keyword(s):

Data Mining ◽

Text Mining ◽

Customer Relationship ◽

Competitive Advantages ◽

Strategic Decisions ◽

Data Mining Tool ◽

Mining Tool ◽

The Moment ◽

Mining Tools

Marketing concept has progressed through different phases of evolution in the past. At the moment, customer relationship management is considered as the last era of marketing development. The main purpose of this approach is to build long-term oriented profitable relationships with customers. So, companies should know better their customers. This knowledge can be created through a deeper analysis of companies' data with data mining tools. Companies which are able to use data mining tools will gain strong competitive advantages for their strategic decisions. Hotel industry is selected in this study, since it provides a warehouse of customer comments from which precious knowledge can be obtained if text mining as a data mining tool is used appropriately. Thus, this study attempts to explain the stages of text mining with the use of Rapidminer. As a result, different approaches according to the customer satisfaction/dissatisfaction are discussed to build competitive advantages.

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Text Mining Tools: Techniques and Visualizations

Exploring Big Historical Data ◽

10.1142/9781783266104_0003 ◽

2015 ◽

pp. 73-111

Keyword(s):

Text Mining ◽

Mining Tools

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Text Mining, Tools

Encyclopedia of Systems Biology ◽

10.1007/978-1-4419-9863-7_181 ◽

2013 ◽

pp. 2160-2162

Author(s):

Jörg Hakenberg

Keyword(s):

Text Mining ◽

Mining Tools

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Text mining tools for extracting information about microbial biodiversity in food

Food Microbiology ◽

10.1016/j.fm.2018.04.011 ◽

2019 ◽

Vol 81 ◽

pp. 63-75 ◽

Cited By ~ 5

Author(s):

Estelle Chaix ◽

Louise Deléger ◽

Robert Bossy ◽

Claire Nédellec

Keyword(s):

Text Mining ◽

Microbial Biodiversity ◽

Mining Tools

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Networks Models of Actin Dynamics during Spermatozoa Postejaculatory Life: A Comparison among Human-Made and Text Mining-Based Models

BioMed Research International ◽

10.1155/2016/9795409 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Nicola Bernabò ◽

Alessandra Ordinelli ◽

Marina Ramal Sanchez ◽

Mauro Mattioli ◽

Barbara Barboni

Keyword(s):

Text Mining ◽

Literature Search ◽

Human Spermatozoa ◽

Actin Dynamics ◽

Hierarchical Architecture ◽

Scale Free ◽

Fertilizing Ability ◽

Actin Remodelling ◽

Mining Tools ◽

Biological Context

Here we realized a networks-based model representing the process of actin remodelling that occurs during the acquisition of fertilizing ability of human spermatozoa (HumanMade_ActinSpermNetwork, HM_ASN). Then, we compared it with the networks provided by two different text mining tools: Agilent Literature Search (ALS) and PESCADOR. As a reference, we used the data from the online repository Kyoto Encyclopaedia of Genes and Genomes (KEGG), referred to the actin dynamics in a more general biological context. We found that HM_ALS and the networks from KEGG data shared the same scale-free topology following the Barabasi-Albert model, thus suggesting that the information is spread within the network quickly and efficiently. On the contrary, the networks obtained by ALS and PESCADOR have a scale-free hierarchical architecture, which implies a different pattern of information transmission. Also, the hubs identified within the networks are different: HM_ALS and KEGG networks contain as hubs several molecules known to be involved in actin signalling; ALS was unable to find other hubs than “actin,” whereas PESCADOR gave some nonspecific result. This seems to suggest that the human-made information retrieval in the case of a specific event, such as actin dynamics in human spermatozoa, could be a reliable strategy.

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