A Survey of Deep Learning for Sentiment Analysis
Abstract
Deep learning has detonated in the public
responsiveness, primarily as predictive and analytical products
pervade our world, in the form of innumerable humancentered
smart-world systems, including targeted
advertisements, natural language assistants and interpreters,
and mock-up self-driving vehicle systems. In contrast,
researchers across disciplines have been including into their
research to solve various natural language processing issues. In
this paper we seek to provide a thorough exploration of Deep
learning and its applications like sentimental analysis and
natural language processing (NLP). Deep learning has an edge
over the traditional machine learning algorithms, like support
vector machine (SVM) and Naïve Bayes, for sentiment analysis
because of its potential to overcome the challenges faced by
sentiment analysis and handle the diversities involved, without
the expensive demand for manual feature engineering. Deep
learning models promise one thing - given sufficient amount of
data and sufficient amount of training time, they can perform
the task of sentiment classification on any text class with
minimal restrictions and no task-specific or data-specific
manual feature engineering. We hope this survey provides a
valuable reference for new Deep learning practitioners, as well
as those seeking to innovate in the application of deep learning.
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