Sentiment Recognition in Customer Reviews Using Deep Learning

Sentiment Recognition in Customer Reviews Using Deep Learning

Vinay Kumar Jain, Shishir Kumar, Prabhat Mahanti
Copyright: © 2018 |Pages: 10
DOI: 10.4018/IJEIS.2018040105
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Abstract

Deep learning has become popular in all aspect related to human judgments. Most machine learning techniques work well which includes text classification, text sequence learning, sentiment analysis, question-answer engine, etc. This paper has been focused on two objectives, firstly is to study the applicability of deep neural networks strategies for extracting sentiment present in social media data and customer reviews with effective training solutions. The second objective is to design deep networks that can be trained with these weakly supervised strategies in order to predict meaningful inferences. This paper presents the concept and steps of using deep learning for extraction sentiments from customer reviews. The extraction pulls out the features from the customer reviews using deep learning popular methods including Convolution neural networks (CNN) and Long Short-Term Memory (LSTM) architectures. The comparison of the results with tradition text classification method such as Naive Bayes(NB) and Support Vector Machine(SVM) using two data sets IMDB reviews and Amazon customer reviews have been presented. This work mainly focused on investigating the merit of using deep models for sentiment analysis in customer reviews.
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Background

The growth of unstructured data has interested researchers from various disciplines to uncover the hidden knowledge by applying intelligent data analysis techniques. There have been extensive of publications in this area, notably in marketing as well as computer science. There are two main types of textual information on the web: facts and opinions. On one hand, facts are assumed to be true and on the other, opinions express subjective information about a certain entity or topic (Chesley et.al, 2006).

Sentiment analysis has been as of late seen a lot of enthusiasm from mainstream researchers (Gordon, 2013). The domain of sentiment analysis has customarily been connected to a solitary area at any given moment, for example, movie reviews or product reviews (Pang and Lee, 2008). All the more as of late, much exertion has been put into the improvement of sentiment analysis techniques that can be utilized over different areas like movie and product reviews, election result prediction; disease outbreak, stock market etc. (Pang and Lee, 2008).

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