Abstract
Sentiment analysis involves classifying text into positive, negative and neutral classes according to the emotions expressed in the text. Extensive study has been carried out in performing sentiment analysis using the traditional ‘bag of words’ approach which involves feature selection, where the input is given to classifiers such as Naive Bayes and SVMs. A relatively new approach to sentiment analysis involves using a deep learning model. In this approach, a recently discovered technique called word embedding is used, following which the input is fed into a deep neural network architecture. As sentiment analysis using deep learning is a relatively unexplored domain, we plan to perform in-depth analysis into this field and implement a state of the art model which will achieve optimal accuracy. The proposed methodology will use a hybrid architecture, which consists of CNNs (Convolutional Neural Networks) and RNNs (Recurrent Neural Networks), to implement the deep learning model on the SAR14 and Stanford Sentiment Treebank data sets.
Highlights
In today’s world, reviews of a service provided by a business play an integral role in determining its success
In order to achieve optimal accuracy, we propose a hybrid architecture made up of CNNs and RNNs
Sentiment analysis is a very useful task for businesses to understand the emotions of their customers regarding their products and services
Summary
In today’s world, reviews of a service provided by a business play an integral role in determining its success. A technique that can classify these opinions and emotions into distinct classes or categories would be invaluable to any business, as they would no longer have to pore over millions of reviews manually to understand the success of their service as per the voice of their customers Sentiment analysis is such a technique, in which text is broadly classified into three classes; positive, negative and neutral. The Stanford Sentiment Treebank dataset consists of 11855 sentences extracted from Rotten Tomatoes movie reviews and has fully labeled parse trees with five classes as labels (extremely negative to extremely positive). It has 8544 training examples, 1101 validation samples and 2210 test cases. In order to achieve optimal accuracy, we propose a hybrid architecture made up of CNNs and RNNs
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More From: International Journal of Advanced Research in Computer Science
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