Tree-Structured Regional CNN-LSTM Model for Dimensional Sentiment Analysis

Yu, Liang-Chih; Lai, K. Robert; Wang, Jin

doi:10.1109/taslp.2019.2959251

Cited by 104 publications

(40 citation statements)

References 41 publications

(68 reference statements)

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“…In another study, Wang et al [70] presented a hybrid deep learning-based scheme for sentiment analysis on the basis of convolutional neural networks and long short-term memory. Similarly, Wang et al [69] introduced a stacked residual long short-term memory-based architecture to identify sentiment intensity of text documents.…”

Section: Related Workmentioning

confidence: 99%

Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach

Onan

2020

Comp Applic In Engineering

263

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Massive open online courses (MOOCs) are recent innovative approaches in distance education, which provide learning content to participants without age-, gender-, race-, or geography-related barriers. The purpose of our research is to present an efficient sentiment classification scheme with high predictive performance in MOOC reviews, by pursuing the paradigms of ensemble learning and deep learning. In this contribution, we seek to answer several research questions on sentiment analysis on educational data. First, the predictive performance of conventional supervised learning methods, ensemble learning methods and deep learning methods has been evaluated. Besides, the efficiency of text representation schemes and word-embedding schemes has been evaluated for sentiment analysis on MOOC evaluations. For the evaluation task, we have analyzed a corpus containing 66,000 MOOC reviews, with the use of machine learning, ensemble learning, and deep learning methods.The empirical analysis indicate that deep learning-based architectures outperform ensemble learning methods and supervised learning methods for the task of sentiment analysis on educational data mining. For all the compared configurations, the highest predictive performance has been achieved by long short-term memory networks in conjunction with GloVe word-embedding scheme-based representation, with a classification accuracy of 95.80%.

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Section: Related Workmentioning

confidence: 99%

Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach

Onan

2020

Comp Applic In Engineering

263

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“…In Table I, the features and challenges of existing Judgment prediction works are enlisted. [37] in 2019 implemented a regional CNN with LSTM model which comprises of two parts: to predict the Valency Arousal ratings of texts on Stanford Sentiment Treebank 1 dataset. The local information within the sentences is observed using regional CNN and long-distance dependencies are extracted by using LSTM across sentences that can be considered in the prediction process.…”

Section: B Limitationsmentioning

confidence: 99%

A Meta Analysis of Attention Models on Legal Judgment Prediction System

Sukanya¹,

J.Priyadarshini²

2021

IJACSA

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Intelligence in legal research is transforming the legal area in manifold ways. Pendency of court cases is a long-lasting problem in the judiciary due to various reasons such as lack of judges, lack of technology in legal services and the legal loopholes. The judicial system has to be more competent and more reliable in providing justice on time. One of the major causes of pending cases is the lack of legal intelligence to assist the litigants. The study in this paper reviews the challenges faced by judgment prediction system due to lengthy case facts using deep learning model. The Legal Judgment prediction system can help lawyers, judges and civilians to predict the win or loss rate, punishment term and applicable law articles for new cases. Besides, the paper reviews current encoding and decoding architecture with attention mechanism of transformer model that can be used for Legal Judgment Prediction system. Natural Language Processing using deep learning is an exploring field and there is a need for research to evaluate the current state of the art at the intersection of good text processing and feature representation with a deep learning model. This paper aims to develop a systematic review of existing methods used in the legal judgment prediction system and about the Hierarchical Attention Neural network model in detail. This can also be used in other applications such as legal document classification, sentimental analysis, news classification, text translation, medical reports and so on.

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“…The CNN-LSTM networks have been applied to tackle a variety of time series prediction and classification problems successfully, e.g. stock market forecasting [77], named entity recognition [78], textual sentiment analysis [79,80], machine translation [81], facial expression recognition [82], and image description generation [83].…”

Section: B the Proposed Cnn-lstm Architecturementioning

confidence: 99%

Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

2020

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In this research, we propose an enhanced Grey Wolf Optimizer (GWO) for designing the evolving Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) networks for time series analysis. To overcome the probability of stagnation at local optima and a slow convergence rate of the classical GWO algorithm, the newly proposed variant incorporates four distinctive search mechanisms. They comprise a nonlinear exploration scheme for dynamic search territory adjustment, a chaotic leadership dispatching strategy among the dominant wolves, a rectified spiral local exploitation action, as well as probability distribution-based leader enhancement. The evolving CNN-LSTM models are subsequently devised using the proposed GWO variant, where the network topology and learning hyperparameters are optimized for time series prediction and classification tasks. Evaluated using a number of benchmark problems, the proposed GWO-optimized CNN-LSTM models produce statistically significant results over those from several classical search methods and advanced GWO and Particle Swarm Optimization variants. Comparing with the baseline methods, the CNN-LSTM networks devised by the proposed GWO variant offer better representational capacities to not only capture the vital feature interactions, but also encapsulate the sophisticated dependencies in complex temporal contexts for undertaking time-series tasks.

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Tree-Structured Regional CNN-LSTM Model for Dimensional Sentiment Analysis

Cited by 104 publications

References 41 publications

Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach

Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach

A Meta Analysis of Attention Models on Legal Judgment Prediction System

Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer

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