Discovering novel memory cell designs for sentiment analysis on tweets

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 17,000 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 cumulated accuracies on all three considered tasks.