Author attribution has grown into an area that is more challenging from the past decade. It has become an inevitable task in many sectors like forensic analysis, law, journalism and many more as it helps to detect the author in every documentation. Here unigram/bigram features along with latent semantic features from word space were taken and the similarity of a particular document was tested using Random forest tree, Logistic Regression and Support Vector Machine in order to create a global model. Dataset from PAN Author Identification shared task 2014 is taken for processing. It has been observed that the proposed model shows state-of-art accuracy of 80% which is significantly greater when compared to the Author Identification PAN results of the year 2014. © 2015 IEEE.
H. B. Barathi Ganesh, Reshma, U., and M. Kumar, A., “Author identification based on word distribution in word space”, in 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, ICACCI 2015; SCMS Group of Institutions, Corporate Office CampusPrathap Nagar , Muttom, Aluva, Kochi (Ernakulam)Kerala; India, 2015.