Text Analysis: An Introduction

Text Analysis at a very basic level tries to process textual information by using a combination of software, methodologies, processes-metrics, information visualization and most importantly human insights. Text Analysis in a way is a very modest attempt to understand how the human brain processes information and an attempt at automating some limited sections of these processes. Text Analysis tries to fuse automation with human understanding and human analytical capabilities. Text Analysis is not just about software - software or technology in this context is just a tool to aid human analysis - Text Analysis methodology besides software, draws from a number of other fields (cognitive psychology, linguistics, semantics, mathematics, decision sciences and statistics) to analyze text either in relationship with or in contrast to logically formatted structured numerical information to derive actionable information intelligence that can be acted upon by real people.

Text Analysis and Text Mining

Trying to distinguish between text mining and Text Analysis could prove to be as tricky as the chicken-and-egg problem, but it is possible to point out a few differentiating factors:

• Text mining applications available in the technology market today, usually consider documents as processing units, whereas, Text Analysis (as conceived at K-Praxis) uses more strategic-level units such as sentences, individual relationship between words and other linguistic and information entities as processing units.
• Most of the text mining applications available in the market - to large extent - do not analyze text base information - rather than analysis these applications spend most of the computing energy in classifying documents. Unless you are talking about few areas like intelligent agencies or big corporations, this classification capability does not provide any actionable inputs that could aid or make tangible differences to human decision making. On the other hand, Text Analysis deals with and analyzes smaller information units (and their inter-relationships) that could provide you with timely relevant actionable information - aiding human capability in strategic decision making
• Text Analysis based automatic processing of textual information may not involve some very complicated AI based technology, but draws from various fields of knowledge to provide a holistic basis for decision support.

Sales Marketing Intelligence: Is your company looking to buy a Sales or Marketing Intelligence solution? Then its time you analyze the solution from a Text Analysis point of view. A report by K-Praxis on Sales and Marketing Intelligence provides a roadmap for integrating Text Analysis with traditional data mining. The complete report (Sales and Marketing Intelligence: The Need for Integrating Textual Analytics with Traditional Solutions) is available for purchase through InfoSphere AB .

Text Analysis and Data Mining

The relationship between Text Analysis and data mining is that of a complementary nature. Traditionally, it is thought data mining deals with structured (pre-formatted and numerical data), and text analysis (rather, text mining as text analysis is a new category) deals with textual information. But, both of these fields if made to work in complementary fashion could draw from each other to provide a significant helping hand to humans in aiding their strategic decisions requirements. It is important to note that textual information many a times could contain numerical or pre-formatted information and Text Analysis as a technology category will be required to connect this structured information to unstructured free flow text.

Text Analysis and Opinion Mining

Text Analysis is very amenable to next level of advancement in information processing - mining opinions and feeling expressed in textual information. This is one aspect of textual and natural language based information that is most difficult to handle. At K-Praxis, we are developing methodologies and metrics to deal with opinions and feelings and connecting them to data and text. A combination of data mining, Text Analysis and opinion mining creates a formidable decision making solution that could help organizations cut down costs at various levels of their operations.

Text Analysis: Applications

Given this back-ground, Text Analysis as a technology category could be applied to a variety of contexts. The most unique advantage of Text Analysis is that it goes beyond traditional bounds of research, it takes textual information to the heart of an organization's actionable information and to the strategic decision making requirement-level - removing the traditional gap between research and practice. Text Analysis based solutions are very similar to strategic decision support systems - yet, in a way offer much more cohesive decision support than proffered by the traditional decision support systems.

Text Analysis thus could benefit any organization that is looking towards leveraging their content assets, with the aim of deriving actionable and timely relevant information. This hypothesis basically puts it at the center of any commercial activity where abstracting insights from information is of high importance - so whether one is required to process information to detect the possible causes of a Columbia disaster or one needs to do it to understand what direction a company like Google could take ( Pre-IPO Google Dossier: An Experiment in Opinion Mining ).

The complete Pre-IPO Google Dossier: An Experiment in Opinion Mining is available on request. The complete package includes: The hypotheses discovered through this text analysis and opinion mining experiment Opinion clusters and hypotheses visualization An Executive summary in a power point presentation format Contact us to get your copy Contact us to get your copy of the report. .