Stacked denoising autoencoders for sentiment analysis: a review

Sagha, Hesam; Cummins, Nicholas; Schuller, Björn

doi:10.1002/widm.1212

Cited by 31 publications

(13 citation statements)

References 34 publications

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“…As shown, deep learning outperformed the four classifiers used in sentiment classification. These results aligned with [34] findings that DL outperformed a number of machine learning algorithms in image processing and text mining. The bar chart in Figure 2 shows the classification accuracy of the five models as a percentage.…”

Section: Online Reviewssupporting

confidence: 84%

Sentiment Analysis towards Actionable Intelligence via Deep Learning

Eletter¹

2020

TEM Journal

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The exponential growth of unstructured data and the ability of businesses to utilize such data in decision-making have led to competitive advantages. The knowledge provided by analyzing unstructured data is crucial for product developers or service providers because it might affect the sustainability of the business. Sentiment analysis is used to gain an understanding of the attitudes, opinions, and emotions expressed within an online review. Naïve Bayes (NB), logistic regression (LR), decision trees (DT), deep learning (DL), and support vector machines (SVM) were used to build a classification model. In the data mining settings, the classification accuracy is the best metric to highlight the best classifier. The DL classifier outperformed other models in terms of accuracy rate. Classifying customers' feelings toward a product or service is critical for providing actionable insights. Utilizing such models will help to analyze huge volumes of reviews, saving both time and costs.

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Section: Online Reviewssupporting

confidence: 84%

Sentiment Analysis towards Actionable Intelligence via Deep Learning

Eletter¹

2020

TEM Journal

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“…For humans to trust these models, they need to be interpretable and explainable. Multiple publications [26,118] have reported that a neural network could be fooled easily into choosing a wrong category by making minor changes to pixels and neither discriminative nor generative models are an exception. Interpretability alone might not be enough for humans to trust these black box models; they will need explainability.…”

Section: Generalization Powermentioning

confidence: 99%

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

Soother

Daudpoto

Harris

et al. 2021

Mathematical Problems in Engineering

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The advent of deep learning (DL) has transformed diagnosis and prognosis techniques in industry. It has allowed tremendous progress in industrial diagnostics, has been playing a pivotal role in maintaining and sustaining Industry 4.0, and is also paving the way for industry 5.0. It has become prevalent in the condition monitoring of industrial subsystems, a prime example being motors. Motors in various applications start deteriorating due to various reasons. Thus, the monitoring of their condition is of prime importance for sustaining the operation and maintaining efficiency. This paper presents a state-of-the-art review of DL-based condition monitoring for motors in terms of input data and feature processing techniques. Particularly, it reviews the application of various input features for the effectiveness of DL models in motor condition monitoring in the sense of what problems are targeted using these feature processing techniques and how they are addressed. Furthermore, it discusses and reviews advances in DL models, DL-based diagnostic methods for motors, hybrid fault diagnostic techniques, points out important open challenges to these models, and signposts the prospective future directions for DL models. This review will assist researchers in identifying research gaps related to feature processing, so that they may effectively contribute toward the implementation of DL models as applied to motor condition monitoring.

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“…e semantic orientation method focuses on the subtraction of sentiment words and judgment of the sentiment polarity. erefore, it does not require training beforehand [26]. Stacked denoising autoencoders (SDAs) were used to provide an infrastructure to resolve issues of sentiment recognition from textual contents.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Mining Algorithm for Demand Forecast Analysis on Flash Sales Platform

Zhang

Yixin

2021

Complexity

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With the development of the digital economy, the emerging marketing strategy of the e-commerce flash sales has been changing the traditional purchasing habits of customers. This imposes new decision-making challenges for companies involved in flash sales. It is important for companies to build the accurate product demand forecast analysis focusing on the characteristics of the flash sales and customer behaviors. In this paper, VIPS (Weipinhui, a Chinese e-commerce platform) is taken as a case study with the key focus on how sentiment factors in customer reviews affect product demand in flash sale platforms. The paper adopts two sentiment analysis methods based on emotional dictionaries. The method with a higher evaluation index is adopted to integrate the emotional factors into the autoregressive model for product demand and assessment. The experiments prove that the autoregressive model for integrating the sentiment factors demonstrates better forecasting performances than the models without sentiment factors. The experiments further confirm that when product demand for the previous two weeks and customer review sentiment factors in the previous week are taken into consideration, demand forecast effects are most accurate.

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Stacked denoising autoencoders for sentiment analysis: a review

Cited by 31 publications

References 34 publications

Sentiment Analysis towards Actionable Intelligence via Deep Learning

Sentiment Analysis towards Actionable Intelligence via Deep Learning

The Importance of Feature Processing in Deep-Learning-Based Condition Monitoring of Motors

Data Mining Algorithm for Demand Forecast Analysis on Flash Sales Platform

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