Open AccessKeras Model for Text Classification in Amazon Review Dataset using LSTM
Author(s) -
S. Thivaharan,
G. Srivatsun
Publication year - 2021
Publication title -
journal of artificial intelligence and copsule networks
Language(s) - English
Resource type - Journals
ISSN - 2582-2012
DOI - 10.36548/jaicn.2021.2.001
Subject(s) - python (programming language) , computer science , amazon rainforest , sentiment analysis , artificial intelligence , long short term memory , machine learning , data mining , natural language processing , artificial neural network , recurrent neural network , operating system , ecology , biology
With the use of Ecommerce, Industry 4.0 is being effectively used in online product-based commercial transactions. An effort has been made in this article to extract positive and negative sentiments from Amazon review datasets. This will give an upper hold to the purchaser to decide upon a particular product, without considering the manual rating given in the reviews. Even the number words in an inherent positive review exceeds by one, where the present classifiers misclassify them under negative category. This article addresses the aforementioned issue by using LSTM (Long-Short-Term-Memory) model, as LSTM model has a feedback mechanism based progression unlike the other classifiers, which are dependent on feed-forward mechanism. For achieving better classification accuracy, the dataset is initially processed and a total of 100239 short and 411313 long reviews have been obtained. With the appropriate Epoch iterations, it is observed that, this proposed model has gain the ability to classify with 89% accuracy, while maintaining a non-bias between the train and test datasets. The entire model is deployed in TensorFlow2.1.0 platform by using the Keras framework and python 3.6.0.