WEB-BASED SHARE MARKET SENTIMENTAL ANALYSIS USING CATSEEKER ALGORITHM

Abstract

Sentimental analysis play a major role in data mining and research .The process of extracting the
sentiment is an important step in the opinion mining in various web related task such as information retrieval
(IR), information extraction, forecasting and prediction analysis and social network extraction. A supervised
relation extraction system that is trained to extract a particular relation type (source relation) might not
accurately extract a new type of a relation (target relation) for which it has not been trained. However, it is
costly to create training data manually for every new intent expression type that one might want to extract.
We propose a method to adapt an existing sentimental extraction system to extract new features with
minimum supervision for opinion identification. Our proposed method comprises two stages: the semantic
orientation and rule based method using latent semantic analysis. They further improved by using centroidbased actionable 3D subspace clustering framework, named CATSeeker, which allows incorporation of
domain knowledge, and achieves parameter insensitivity and excellent performance through a unique
combination of singular value decomposition, numerical optimization, and 3D frequent item set mining.
Experimental results show that CATSeeker significantly outperforms all the competing methods in terms of
efficiency, parameter insensitivity, and cluster usefulness.

Author

Jeneessha P , J. Mannar Mannan