ICS: TOTAL FREEDOM IN MANUAL TEXT CLASSIFICATION SUPPORTED BY UNOBTRUSIVE MACHINE LEARNING

Abstract : We present the Interactive Classification System (ICS), a web-based application that supports the activity of manual text classification.The application uses machine learning to continuously fit automatic classification models that are in turn used to actively support its users with classification suggestions. The key requirement we have established for the development of ICS is to give its users total freedom of action: they can at any time modify any classification schema and any label assignment, possibly reusing any relevant information from previous activities.

 EXISTING SYSTEM :

 ? The rest of the paper is organized as follows. Section II presents the related work, framing the context of our work and comparing ICS with similar existing systems. ? Each column identifies a functionality: fitting a machine learning model from a training set (batch learning); updating an existing model using novel examples (online learning); the possibility of implementing an active learning process; creating a model starting from an existing one; modifying a schema, i.e., adding/renaming/removing a label

 DISADVANTAGE :

 ? As detailed in the rest of the paper, this approach is a challenge that asks for original machine learning methods to effectively and efficiently solve the problems it poses. ? we present experiments that evaluate the machine learning component of ICS, on four relevant classification problems ? Research on automatic text classification addresses several machine learning problems and scenarios, each defined by a specific set of boundary conditions on the task. ? Relevant to our work is the problem of considering the human as an oracle that acts on-demand, waiting for interrogations by the system.

 PROPOSED SYSTEM :

 ? We show with experiments that the proposed solution is efficient, effective, and competitive against traditional approaches that are not constrained to the requirements of ICS. ? The availability of this classification enables to perform various tasks on the data, e.g. to measure the variation of the engagement of the public over time and topics in order to identify which are the most relevant ones; to profile users’ interests, possibly targeting each different profile with different messages; to select the content that is relevant to one specific topic in order to perform further analysis, e.g., sentiment analysis

 ADVANTAGE :

 ? The application uses machine learning to continuously fit automatic classification models that are in turn used to actively support its users with classification suggestions ? Such automatic classifications can be used as suggestions presented to the users during their manual classification activity, thus implementing an active learning process, or to produce, once its accuracy is deemed to be adequate, the automatic classification of an entire dataset ? This paper follows the terminology currently used in machine learning research.

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