  Text classification plays a vital role today especially with the intensive
use of social networking media. Recently, different architectures of
convolutional neural networks have been used for text classification in which
one-hot vector, and word embedding methods are commonly used. This paper
presents a new language independent word encoding method for text
classification. The proposed model converts raw text data to low-level feature
dimension with minimal or no preprocessing steps by using a new approach called
binary unique number of word "BUNOW". BUNOW allows each unique word to have an
integer ID in a dictionary that is represented as a k-dimensional vector of its
binary equivalent. The output vector of this encoding is fed into a
convolutional neural network (CNN) model for classification. Moreover, the
proposed model reduces the neural network parameters, allows faster computation
with few network layers, where a word is atomic representation the document as
in word level, and decrease memory consumption for character level
representation. The provided CNN model is able to work with other languages or
multi-lingual text without the need for any changes in the encoding method. The
model outperforms the character level and very deep character level CNNs models
in terms of accuracy, network parameters, and memory consumption; the results
show total classification accuracy 91.99% and error 8.01% using AG's News
dataset compared to the state of art methods that have total classification
accuracy 91.45% and error 8.55%, in addition to the reduction in input feature
vector and neural network parameters by 62% and 34%, respectively.
