ABSTRACT:
Despite recent successes of deep
learning in many fields of natural language processing, previous studies of
emotion recognition on Twitter mainly focused on the use of lexicons and simple
classifiers on bag-of-words models. The central question of our study is
whether we can improve their performance using deep learning. To this end, we
exploit hash tags to create three large emotion-labeled data sets corresponding
to different classifications of emotions. We then compare the performance of
several word and character-based recurrent and convolutional neural networks
with the performance on bag-of-words and latent semantic indexing models. We
also investigate the transferability of the final hidden state representations
between different classifications of emotions, and whether it is possible to
build a unison model for predicting all of them using a shared representation.
We show that recurrent neural networks, especially character-based ones, can
improve over bag-of-words and latent semantic indexing models. Although the
transfer capabilities of these models are poor, the newly proposed training
heuristic produces a unison model with performance comparable to that of the
three single models.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
Niko Colneriˇc
and Janez Demˇsar, “Emotion Recognition on Twitter: Comparative Study and
Training a Unison Model”, IEEE Transactions on Affective Computing, 2018.