32,773 research outputs found
Basic tasks of sentiment analysis
Subjectivity detection is the task of identifying objective and subjective
sentences. Objective sentences are those which do not exhibit any sentiment.
So, it is desired for a sentiment analysis engine to find and separate the
objective sentences for further analysis, e.g., polarity detection. In
subjective sentences, opinions can often be expressed on one or multiple
topics. Aspect extraction is a subtask of sentiment analysis that consists in
identifying opinion targets in opinionated text, i.e., in detecting the
specific aspects of a product or service the opinion holder is either praising
or complaining about
A Theoretical Analysis of Deep Neural Networks for Texture Classification
We investigate the use of Deep Neural Networks for the classification of
image datasets where texture features are important for generating
class-conditional discriminative representations. To this end, we first derive
the size of the feature space for some standard textural features extracted
from the input dataset and then use the theory of Vapnik-Chervonenkis dimension
to show that hand-crafted feature extraction creates low-dimensional
representations which help in reducing the overall excess error rate. As a
corollary to this analysis, we derive for the first time upper bounds on the VC
dimension of Convolutional Neural Network as well as Dropout and Dropconnect
networks and the relation between excess error rate of Dropout and Dropconnect
networks. The concept of intrinsic dimension is used to validate the intuition
that texture-based datasets are inherently higher dimensional as compared to
handwritten digits or other object recognition datasets and hence more
difficult to be shattered by neural networks. We then derive the mean distance
from the centroid to the nearest and farthest sampling points in an
n-dimensional manifold and show that the Relative Contrast of the sample data
vanishes as dimensionality of the underlying vector space tends to infinity.Comment: Accepted in International Joint Conference on Neural Networks, IJCNN
201
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