Much of the ongoing explosion of digital content is in the form of text. This content is a virtual gold-mine of information that can inform a range of social, governmental, and business decisions. For example, using content available on blogs and social networking sites businesses can find out what its customers are saying about their products and services. In the digital age where customer is king, the business value of ascertaining consumer sentiment cannot be overstated. People express sentiments in myriad ways. At times, they use simple, direct assertions, but most often they use sentences involving comparisons, conjunctions expressing multiple and possibly opposing sentiments about multiple features and entities,and pronominal references whose resolution requires discourse level context. Frequently people use abbreviations, slang, SMSese, idioms and metaphors. Understanding the latter also requires common sense reasoning. In this paper, we present iSEE, a fully implemented sentiment extraction engine, which makes use of statistical methods, classical NLU techniques, common sense reasoning, and probabilistic inference to extract entity and feature specific sentiment from complex sentences and dialog. Most of the components of iSEE are domain independent and the system can be generalized to new domains by simply adding domain relevant lexicons.

This paper formulates a collaborative system for modeling business application. The system uses a Semantic Wiki to enable collaboration between the various stakeholders involved in the design of the system and translates the captured intelligence into business models which are used for designing a business system.

In the paper, we present an approach of image processing analysis to extract flowchart information from digital imagery. Firstly, flowchart imagery is processed to extract the text components and then extract the geometrical shapes components. We analyze text, and various geometrical shapes present in flowchart and carry out a variety of processes such as image segmentation, shape description, text and geometric components extraction, recognition and linking. The text components are extracted and then geometrical components are extracted. we also proposed a auto directional transformation of contour chain method for shape description. The internal relationship between the components is set up by tracing the flow lines which connect different components. Thus a flowchart is correctly extracted. The extracted components are output to metadata (XML format) which is machine readable. These metadata can be archived, store as knowledge base or shared with others. Finally, an example is presented and the results show that the proposed technique is efficient.