Open AccessEnhanced Long Short-Term Memory (ELSTM) Model for Sentiment Analysis
Author(s) -
Dimple Tiwari,
Bharti Nagpal
Publication year - 2021
Publication title -
the international arab journal of information technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.227
H-Index - 27
eISSN - 2309-4524
pISSN - 1683-3198
DOI - 10.34028/iajit/18/6/12
Subject(s) - computer science , benchmark (surveying) , sentiment analysis , artificial intelligence , field (mathematics) , machine learning , feature (linguistics) , feature extraction , term (time) , deep learning , process (computing) , set (abstract data type) , data mining , pattern recognition (psychology) , linguistics , philosophy , physics , mathematics , geodesy , quantum mechanics , pure mathematics , programming language , geography , operating system
Sentiment analysis is used to embed an extensive collection of reviews and predicts people's opinion towards a particular topic, which is helpful for decision-makers. Machine learning and deep learning are standard techniques, which make the process of sentiment analysis simpler and popular. In this research, deep learning is used to analyze the sentiments of people. It has an ability to perform automatic feature extraction, which provides better performance, a more vibrant appearance, and more reliable results than conventional feature-based techniques. Traditional approaches were based on complicated manual feature extractions that were not able to provide reliable results. Therefore, the presented study aimed to improve the performance of the deep learning approach by combining automatic feature extraction with manual feature extraction techniques. The enhanced ELSTM model is proposed with hyper-parameter tuning in previous Long Short-Term Memory (LSTM) to get better results. Based on the results, a novel model of sentiment analysis and novel algorithm are proposed to set the benchmark in the field of textual classification and to describe the procedure of the developed model, respectively. The results of the ELSTM model are presented by training and testing accuracy curve. Finally, a comparative study confirms the best performance of the proposed ELSTM model.