Seq2Seq Deep Learning Models for Microtext Normalization

Ranjan Satapathy, Yang Li, Sandro Cavallari, Erik Cambria

Abstract: Microtext analysis is a crucial task for gauging social media opinion. In this paper, we compare four different deep learning encoder-decoder frameworks to handle microtext normalization problem. The frameworks have been evaluated on four different datasets in three different domains. To understand the impact of microtext normalization, we further integrate the framework into a sentiment classification task. This paper is the first of its kind to incorporate deep learning into a microtext normalization module and improve the sentiment analysis task. We show our models as a sequence to sequence character to word encoder-decoder model. We compare four deep learning models for microtext normalization task which further improve the accuracy of the sentiment analysis. Results show that the attentive LSTM and GRU cell both increase the sentiment analysis accuracy in the range of 4%–7% whereas LSTM and CNN with LSTM improve the accuracy in the range of 2%–4%.