Implement a neural network based algorithm, which can classify some speciﬁc attribute via nature
languages and present the results with visualization techniques.
• Collect and select appropriate dataset
• Preprocess dataset
• Train neural network via nature language
• Use neural network to classify predict attribute of books
• Visualization of data and result
Description of the project
The main aim of this project is to design a speciﬁc neural network to classify attribute(e.g genre)
via natural language(e.g. the summary of books). A neural network is a network of neurons
composed of artiﬁcial nodes. Normally, a neural network has at least three layers, input layer,
several hidden layers and output layer. This artiﬁcial network may used for classifying or predict
where it can be trained by dataset. This self-learning network can extract internal connections
from seemingly unrelated data and generate reasonable predictions through extensive training.
Thus it is theoretically feasible to use neural network to classify the some attribute of books via
In today’s world, Amazon and other bookstores need to make a preliminary of the books which
will be sold in their store, in order to maintain their good reviews. Usually they invite professional
personnel to classify books after reading, but this method is very ineﬃcient when faced with a
large number of books. So they need an algorithm that can handle a large number of book rating
to assist in their work. In many news sites, they also use natural language classify technics to
calculate the category of the corresponding news, which could appear accurately to users on the
pages they browse.
The main point in this project is use eﬃcient and reasonable algorithm to train classify model. This
model need to be trained by nature language and numbers. Now I assume that I need to spend
most of the time in program nature language identify algorithms and select the best from them.
Initial area of research
In the ﬁrst stage, preprocessing dataset is essential, most of machine learning algorithms require
data to be input in the form of matrixes. When I writing this proposal, I think that classify neural
networks(RNN or LSTM) are more incline to supervised learning, so the original dataset needs to
be divided into training set and test set. This step can greatly improved the eﬃciency and
versatility of algorithm sorting with the help of programming language. At the last step of
preprocessing dataset, presenting the abstract and discrete matrix in the form of charts, this step
will be very helpful for future work.
Outcomes of this project is a mature neural network which has been train by thousands times.
This algorithm could used to classify the speciﬁc attribute via natural language. All results from
this algorithm are clearly presented in a visualization method. The accuracy of the algorithm will
be maximized during programming process(it is currently expected to be at least 70%).
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