InforSense
Embedding Intelligence Throughout the Enterprise
 

TEXTSENSE

Realize the insight hidden in text

 

Key Benefits:

  • Improve searching and provide more focussed results set using ontologies
  • Automatically monitor, report and distribute new textual information
  • Rapidly deploy text analytics applications via a portal interface for enterprise-wide access

TextSense is an add-on module for the InforSense platform, which provides text mining capabilities that allow text analytics applications to be rapidly constructed in conjunction with the platforms standard analytical components. TextSense provides the capabilities to analyse documents of all types to identify the most relevant documents, the sentiment of documents, trends within published material and other useful statistics. The resulting text analytics applications can be deployed via Document Explorer, which is a web based document viewer that provides powerful ontology based filtering to ensure a focussed results set. Combined, TextSense and Document Explorer offer a powerful, user-friendly tool for analyzing unstructured data

  • Complete text mining infrastructure including tools for text processing, statistical analysis, information extraction and text mining
  • Easily analyze unstructured data of any type including text, PDFs, HTML, XML and RSS feeds
  • Interpret experimental results and challenge hypotheses by leveraging knowledge held in literature repositories
  • Combine analysis of unstructured data with structured data in a single browser-based environment
  • Ontology driven browsing used to categorize documents, enabling users to quickly find related information

Customer Satisfaction Case Study

Organizations need to understand their customers in order to make profitable business decisions and to decide how to target individual customers. This information is traditionally gathered in the form of surveys. Such surveys gather much richer data if they include free text comments from the respondents, but these can be difficult to process and include in any summarization or predictive modeling of the survey results. Text analytics enables the key facts and customer sentiment to be extracted from these comments, therefore providing a more complete picture of the levels of customer satisfaction and enabling these comments to be combined with other structured survey results for modeling of customer behavior.

Corporate Intelligence Case Study

News feeds (RSS feeds) are a valuable source of up to date information that can be harnessed by an organization to better understand the corporate environment in which they operate. By automatically monitoring and mining a large number of feeds, a company can extract and analyze the information they contain to follow trends and flag up new stories. The feeds can also be aggregated, filtered and categorized to generate novel, customized news feeds for deployment within the enterprise.

Ontology Tagging Case Study

Ontologies are structured vocabularies that are used to describe knowledge (entities and the relationships between these) within a given domain. Well known ontologies include the Gene Ontology in the biomedical domain and the Derwent World Patents Index Codes in the intellectual property domain. Analysts find it much easier to locate relevant literature if it has been categorized against an ontology that describes their domain. This is often done manually, for instance various biomedical databases curate scientific papers according to the Gene Ontology concepts to which they refer. Using text analytics and machine learning techniques, documents from any source can be automatically categorized to any ontology. For instance, as well as accessing patents manually categorized by Derwent World Patents Index Codes, an analyst could also access Medline abstracts, which may contain useful information associated with the patents, categorized by the same codes.

Gene Expression Case Study

Gene expression profiling is widely used for target discovery in the drug development process. Such experiments result in a list of differentially expressed genes which the analyst will wish to investigate further. One information source that can be leveraged for this is the published scientific literature. Text analytics can be used to answer specific questions about the genes; are there direct or indirect relationships between these genes and the disease under study, in which biological processes are these genes involved, in what biological pathways are these genes involved. In this way, hypothesis based on the experimental results, may be supported or contradicted by information extracted from the published scientific literature.

 

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