Reducing Computational Costs in Sentiment Analysis: Tensorized Recurrent
  Networks vs. Recurrent Networks
Gabriel Lopez Anna Nguyen Joe Kaul
Anticipating audience reaction towards a certain text is integral to several
facets of society ranging from politics, research, and commercial industries.
Sentiment analysis (SA) is a useful natural language processing (NLP) technique
that utilizes lexical/statistical and deep learning methods to determine
whether different-sized texts exhibit positive, negative, or neutral emotions.
Recurrent networks are widely used in machine-learning communities for problems
with sequential data. However, a drawback of models based on Long-Short Term
Memory networks and Gated Recurrent Units is the significantly high number of
parameters, and thus, such models are computationally expensive. This drawback
is even more significant when the available data are limited. Also, such models
require significant over-parameterization and regularization to achieve optimal
performance. Tensorized models represent a potential solution. In this paper,
we classify the sentiment of some social media posts. We compare traditional
recurrent models with their tensorized version, and we show that with the
tensorized models, we reach comparable performances with respect to the
traditional models while using fewer resources for the training.