Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications
Volume-3 | Issue-3
Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4
Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3
Hybrid Parallel Image Processing Algorithm for Binary Images with Image Thinning Technique
Volume-3 | Issue-3
Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4
QoS-aware Virtual Machine (VM) for Optimal Resource Utilization and Energy Conservation
Volume-3 | Issue-3
Probabilistic Neural Network based Managing Algorithm for Building Automation System
Volume-3 | Issue-4
Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach
Volume-3 | Issue-1
Artificial Bee Colony Optimization Algorithm for Enhancing Routing in Wireless Networks
Volume-3 | Issue-1
Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4
Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3
Real Time Anomaly Detection Techniques Using PySpark Frame Work
Volume-2 | Issue-1
Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
Volume-3 | Issue-4
Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4
Frontiers of AI beyond 2030: Novel Perspectives
Volume-4 | Issue-4
Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach
Volume-3 | Issue-4
Artificial Intelligence Algorithm with SVM Classification using Dermascopic Images for Melanoma Diagnosis
Volume-3 | Issue-1
An Efficient Machine Learning based Model for Classification of Wearable Clothing
Volume-3 | Issue-4
Volume - 3 | Issue - 2 | june 2021
Published
15 June, 2021
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.
KeywordsEcommerce LSTM model feed-forward mechanism feedback mechanism Keras TensorFlow relu sigmoid GPU device
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