Discovering novel memory cell designs for sentiment analysis on tweets
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- @Article{Nistor:GPEM,
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author = "Sergiu Cosmin Nistor and Mircea Moca and
Razvan Liviu Nistor",
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title = "Discovering novel memory cell designs for sentiment
analysis on tweets",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2021",
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volume = "22",
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number = "2",
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pages = "147--187",
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month = jun,
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keywords = "genetic algorithms, genetic programming, ANN, Memory
cell, Evolutionary algorithm, Deep learning, Recurrent
neural network, Sentiment analysis, Tweet",
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ISSN = "1389-2576",
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DOI = "doi:10.1007/s10710-020-09395-0",
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size = "41 pages",
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abstract = "Designing a Recurrent Neural Network to extract
sentiment from tweets is a very hard task. When using
memory cells in their design, the task becomes even
harder due to the large number of design alternatives
and the costly process of finding a performant design.
In this paper we propose an original evolutionary
algorithm to address the hard challenge of discovering
novel Recurrent Neural Network memory cell designs for
sentiment analysis on tweets. We used three different
tasks to discover and evaluate the designs. We
conducted experiments and the results show that the
best obtained designs surpass the baselines, which are
the most popular cells, LSTM and GRU. During the
discovery process we evaluated roughly 17000 cell
designs. The selected winning candidate outperformed
the others for the overall sentiment analysis problem,
hence showing generality. We made the winner selection
by using the accumulated accuracies on all three
considered tasks.",
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notes = "Department of Computer Science, Babes-Bolyai
University, Cluj-Napoca, Romania",
- }
Genetic Programming entries for
Sergiu Cosmin Nistor
Mircea Moca
Razvan Liviu Nistor
Citations