How textual quality of online reviews affect classification performance: a case of deep learning sentiment analysis

Li, Lin; Goh, Tiong‐Thye; Jin, Dawei

doi:10.1007/s00521-018-3865-7

Cited by 104 publications

(48 citation statements)

References 72 publications

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“…Li et al [27] Estimate the effects of textual quality of online Reviews on classification performance.…”

mentioning

confidence: 99%

WITHDRAWN: Exploring the Effectiveness of Convolutional Neural Network with Ensemble Technique

Ghosh¹,

Sanyal²

2020

Preprint

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Sentiment Analysis has recently been considered as the most active research field in the natural language processing (NLP) domain. Deep Learning is a subset of the large family of Machine Learning and becoming a growing trend due to its automatic learning capability with impressive consequences across different NLP tasks. Hence, a fusion-based Machine Learning framework has been attempted by merging the Traditional Machine Learning method with Deep Learning techniques to tackle the challenge of sentiment prediction for a massive amount of unstructured review dataset. The proposed architecture aims to utilize the Convolutional Neural Network (CNN) with a backpropagation algorithm to extract embedded feature vectors from the top hidden layer. Thereafter, these vectors augmented to an optimized feature set generated from binary particle swarm optimization (BPSO) method. Finally, a traditional SVM classifier is trained with these extended features set to determine the optimal hyper-plane for separating two classes of review datasets. The evaluation of this research work has been carried out on two benchmark movie review datasets IMDB, SST2. Experimental results with comparative studies based on performance accuracy and F-score value are reported to highlight the benefits of the developed frameworks.

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“…Li et al [27] Estimate the effects of textual quality of online Reviews on classification performance.…”

mentioning

confidence: 99%

WITHDRAWN: Exploring the Effectiveness of Convolutional Neural Network with Ensemble Technique

Ghosh¹,

Sanyal²

2020

Preprint

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“…This is a multiclass text categorization problem related to online antisocial behaviour that no other study has undertaken yet, and is the aim of this current research paper. Since deep learning has shown promising results in numerous text classification research problems [39], [69] [44], [70], [71], the technology was extensively experimented with, by using its different variants and parameter settings, to propose a conceptual framework that can be implemented at a scale to detect different types of online antisocial behaviours, and to make online communities safer place for everyone to participate.…”

Section: Antisocial Behavior and Social Mediamentioning

confidence: 99%

Deep Learning for Multi-Class Antisocial Behavior Identification From Twitter

Singh

Subramani

et al. 2020

IEEE Access

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Social Media has become an integral part of our daily life. Not only it enables collaboration and flow of information but has also become an imperative tool for businesses and governments around the world. All this makes a compelling case for everyone to be on some sort of online social media platform. However, this virtuousness is overshadowed by some of its shortcomings. The manifestation of antisocial behaviour online is a growing concern that hinders participation and cultivates numerous social problems. Antisocial behaviour exists in its various forms such as aggression, disregard for safety, lack of remorse, unlawful behaviour, etc. The paper introduces a deep learning-based approach to detect and classify online antisocial behaviour (ASB). The automatic content classification addresses the issue of scalability, which is imperative when dealing with online platforms. A benchmark dataset was created with multi-class annotation under the supervision of a domain expert. Extensive experiments were conducted with multiple deep learning algorithms and their superior results were validated against the results from the traditional machine learning algorithms. Visually enhanced interpretation of the classification process is presented for model and error analyses. Accuracy of up to 99% in class identification was achieved on the ground truth dataset for empirical validation. The study is an evidence of how the cutting-edge deep learning technology can be utilized to solve a real-world problem of curtailing antisocial behaviour, which is a public health threat and a social problem.

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“…Second, non-financial variables are being more widely applied for corporate failure forecasting recently, though financial ratios are still the most popular variables [9,20], such as market information, macroeconomic, industry information, and so on [2,24,34]. With the development of artificial intelligence, some literature has started to adopt textual data to forecast corporate failure [5,27,28]. It can be applied to forecast corporate failure as the complement of financial ratios.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It integrates convolutional neural network oriented deep learning (CNN-DL) and support vector machine (SVM) based on the soft set theory (SS). CNN-DL has theoretically proven to be an excellent tool for textual data mining [27]. It is employed as a basis classifier to forecast with textual data.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine

Pan

Chen

et al. 2019

Energies

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Accurate forecasts of corporate failure in the Chinese energy sector are drivers for both operational excellence in the national energy systems and sustainable investment of the energy sector. This paper proposes a novel integrated model (NIM) for corporate failure forecasting in the Chinese energy sector by considering textual data and numerical data simultaneously. Given the feature of textual data and numerical data, convolutional neural network oriented deep learning (CNN-DL) and support vector machine (SVM) are employed as the base classifiers to forecast using textual data and numerical data, respectively. Subsequently, soft set (SS) theory is applied to integrate outputs of CNN-DL and SVM. Hence, NIM inherits advantages and avoids disadvantages of CNN-DL, SVM, and SS. It is able to improve the forecasting performance by taking full use of textual data and numerical data. For verification, NIM is applied to the real data of Chinese listed energy firms. Empirical results indicate that, compared with benchmarks, NIM demonstrates superior performance of corporate failure forecasting in the Chinese energy sector.

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How textual quality of online reviews affect classification performance: a case of deep learning sentiment analysis

Cited by 104 publications

References 72 publications

WITHDRAWN: Exploring the Effectiveness of Convolutional Neural Network with Ensemble Technique

WITHDRAWN: Exploring the Effectiveness of Convolutional Neural Network with Ensemble Technique

Deep Learning for Multi-Class Antisocial Behavior Identification From Twitter

Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine

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