ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis

By Mohammad Ehsan Basiri, Shahla Nemati, Moloud Abdar, Erik Cambria, U. R. Acharrya. Future generations computer systems, 2021

676 citations recorded by the source corpus.

Abstract Sentiment analysis has been a hot research topic in natural language processing and data mining fields in the last decade. Recently, deep neural network (DNN) models are being applied to sentiment analysis tasks to obtain promising results. Among various neural architectures applied for sentiment analysis, long short-term memory (LSTM) models and its variants such as gated recurrent unit (GRU) have attracted increasing attention. Although these models are capable of processing sequences of arbitrary length, using them in the feature extraction layer of a DNN makes the feature space high dimensional. Another drawback of such models is that they consider different features equally important. To address these problems, we propose an Attention-based Bidirectional CNN-RNN Deep Model (ABCDM). By utilizing two independent bidirectional LSTM and GRU layers, ABCDM will extract both past and future contexts by considering temporal information flow in both directions. Also, the attention mechanism is applied on the outputs of bidirectional layers of ABCDM to put more or less emphasis on different words. To reduce the dimensionality of features and extract position-invariant local features, ABCDM utilizes convolution and pooling mechanisms. The effectiveness of ABCDM is evaluated on sentiment polarity detection which is the most common and essential task of sentiment analysis. Experiments were conducted on five review and three Twitter datasets. The results of comparing ABCDM with six recently proposed DNNs for sentiment analysis show that ABCDM achieves state-of-the-art results on both long review and short tweet polarity classification.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/5a88b8f1ffd87fd900de225f3d7e87c9f53af7df

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In Artificial Intelligence

Citations in the corpus

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, neutral): “In the combination of CNNs and RNNs, ConvNet-RNN networks with the cascaded fusion [230,283] or the ensemble strategy [215] have been proposed for FER, by simultaneously capturing textural and temporal information.”
A survey on sentiment analysis methods, applications, and challenges (2022, background): “Occasionally, document-level sentiment analysis is insufficient for specific uses (Behdenna et al. 2018).”
Deep Learning--based Text Classification (2020, methodology): “[94] proposed an attention-based bidirectional CNN-RNN deep model for sentiment analysis.”
A Review of Generalized Zero-Shot Learning Methods (2020, methodology): “NLP [201], [202] and text analysis [203] are two important application areas of machine learning and deep learning methods.”