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Sentiment Classification Using a Single-Layered BiLSTM Model
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DOI：10.1109/ACCESS.2020.2988550.png)
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Abstract
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This study presents a computationally efficient deep learning model for binary sentiment classification, which aims to decide the sentiment polarity of people & x2019;s opinions, attitudes, and emotions expressed in written text. To achieve this, we exploited three widely practiced datasets based on public opinions about movies. We utilized merely one bidirectional long short-term memory (BiLSTM) layer along with a global pooling mechanism and achieved an accuracy of 80.500 & x0025;, 85.780 & x0025;, and 90.585 & x0025; on MR, SST2 and IMDb datasets, respectively. We concluded that the performance metrics of our proposed approach are competitive with the recently published models, having comparatively complex architectures. Also, it is inferred that the proposed single-layered BiLSTM based architecture is computationally efficient and can be recommended for real-time applications in the field of sentiment analysis.
Keywords:
Machine learning
Sentiment analysis
Feature extraction
Computer architecture
Task analysis
Support vector machines
Computational modeling
Bidirectional long short-term memory
deep learning
long-term dependencies
natural language processing
sentiment analysis
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