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Comparison of Stock Price Prediction Models using Pre-trained Neural Networks
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Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division
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Blockchain Framework for Communication between Vehicle through IoT Devices and Sensors
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Maximizing the Prediction Accuracy in Tweet Sentiment Extraction using Tensor Flow based Deep Neural Networks
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DDOS ATTACK DETECTION IN TELECOMMUNICATION NETWORK USING MACHINE LEARNING
Volume-1 | Issue-1
Gas Leakage Detection in Pipeline by SVM classifier with Automatic Eddy Current based Defect Recognition Method
Volume-3 | Issue-3
Construction of a Framework for Selecting an Effective Learning Procedure in the School-Level Sector of Online Teaching Informatics
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Design an Adaptive Hybrid Approach for Genetic Algorithm to Detect Effective Malware Detection in Android Division
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Machine Learning Algorithms Performance Analysis for VLSI IC Design
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Comparison of Stock Price Prediction Models using Pre-trained Neural Networks
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Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach
Volume-3 | Issue-2
Characterizing WDT subsystem of a Wi-Fi controller in an Automobile based on MIPS32 CPU platform across PVT
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Assimilation of IoT sensors for Data Visualization in a Smart Campus Environment
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LoRa-IoT Focused System of Defense for Equipped Troops [LIFE]
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Design of Data Mining Techniques for Online Blood Bank Management by CNN Model
Volume-3 | Issue-3
Volume - 4 | Issue - 4 | december 2022
Published
01 November, 2022
Aspect-Based Sentiment Analysis is a text analysis technique that classifies the sentiment of a specific aspect in a text. With a rise in internet and social media access, people tend to decide to buy a product or to visit a restaurant based on comments and reviews. Categorical reviews can play a significant role in improving the business of a restaurant if a business entity can capture insights of those reviews and use productively. Since Nepali is a low resource language there is no significant numbers of the dataset for restaurant reviews. Therefore a dataset is created in the work to model a target aspect-based sentiment analysis in restaurant review domain. The dataset comprises of comments extracted from social media sites, websites, and manually prepared data. Data are cleaned, pre-processed then aspects and sentiments are extracted using POS tagging. Sentiments and target categories are classified using word embedding and the deep learning BiLSTM model. The sentiment classification model classifies the sentiments into positive or negative. The target classification model classifies text into 5 target categories Food (F), Price (P), Service (S), Hygiene (H), and Experience (E). The result was found to be good compared to similar models in other domains. The sentiment prediction model and target category prediction model attains the F1 Score of 0.88 and 0.76 respectively.
KeywordsABSA BiLSTM LSTM POS TABSA
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