Consumer-Oriented Tech Mining: Integrating the Consumer Perspective into Organizational Technology Intelligence - The Case of Autonomous Driving

Egger, Marc; Schoder, Detlef

doi:10.24251/hicss.2017.133

Cited by 9 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bertaglia and Nunes take advance of text normalization to remove the noise, the non-standard words like spelling errors, abbreviations, mixed case words, acronyms, internet slang, hashtags and emoticons, which are often in documents, especially the informal UGC (Bertaglia and Nunes, 2016). B clean boilerplate, remove non-UGC and delete duplicates from the consumer reviews to get the not relevant for the investigation of consumer perceptions (Egger and Schoder, 2017). These are not enough for getting through the big data to get the real information we want, which need to understand the messages and get the treasures from the messes.…”

Section: Related Workmentioning

confidence: 99%

Identifying domain relevant user generated content through noise reduction: a test in a Chinese stock discussion forum

Yan

Fan

2017

IDD

View full text Add to dashboard Cite

Purpose Getting high-quality data by removing the noisy data from the user-generated content (UGC) is the first step toward data mining and effective decision-making based on ubiquitous and unstructured social media data. This paper aims to design a framework for revoking noisy data from UGC. Design/methodology/approach In this paper, the authors consider a classification-based framework to remove the noise from the unstructured UGC in social media community. They treat the noise as the concerned topic non-relevant messages and apply a text classification-based approach to remove the noise. They introduce a domain lexicon to help identify the concerned topic from noise and compare the performance of several classification algorithms combined with different feature selection methods. Findings Experimental results based on a Chinese stock forum show that 84.9 per cent of all the noise data from the UGC could be removed with little valuable information loss. The support vector machines classifier combined with information gain feature extraction model is the best choice for this system. With longer messages getting better classification performance, it has been found that the length of messages affects the system performance. Originality/value The proposed method could be used for preprocessing in text mining and new knowledge discovery from the big data.

show abstract

Section: Related Workmentioning

confidence: 99%

Identifying domain relevant user generated content through noise reduction: a test in a Chinese stock discussion forum

Yan

Fan

2017

IDD

View full text Add to dashboard Cite

show abstract

“…Furthermore, it has been and continue to be instrumental in enabling/advancing breakthroughs in various disciplines, including disease diagnosis, real-time language translation, autonomous driving, etc. [8,9,15,20,22,25,30,31,33,33]. Meanwhile, edge computing for Internet of Things (IoT) has been widely studied which requires DNN models with small memory size and efficient computation [17,27,28].…”

Section: Introductionmentioning

confidence: 99%

Quantization of Deep Neural Networks for Accurate Edge Computing

Chen

Qiu

Zhuang

et al. 2021

J. Emerg. Technol. Comput. Syst.

View full text Add to dashboard Cite

Deep neural networks have demonstrated their great potential in recent years, exceeding the performance of human experts in a wide range of applications. Due to their large sizes, however, compression techniques such as weight quantization and pruning are usually applied before they can be accommodated on the edge. It is generally believed that quantization leads to performance degradation, and plenty of existing works have explored quantization strategies aiming at minimum accuracy loss. In this paper, we argue that quantization, which essentially imposes regularization on weight representations, can sometimes help to improve accuracy. We conduct comprehensive experiments on three widely used applications: fully connected network for biomedical image segmentation, convolutional neural network for image classification on ImageNet, and recurrent neural network for automatic speech recognition, and experimental results show that quantization can improve the accuracy by 1%, 1.95%, 4.23% on the three applications respectively with 3.5x-6.4x memory reduction.

show abstract

A Multiplex Network Framework Based Recommendation Systems for Technology Intelligence

Yuehgoh

Djebali

Travers

2022

New Trends in Database and Information Systems

View full text Add to dashboard Cite

Consumer-Oriented Tech Mining: Integrating the Consumer Perspective into Organizational Technology Intelligence - The Case of Autonomous Driving

Abstract: Abstract

Cited by 9 publications

References 26 publications

Identifying domain relevant user generated content through noise reduction: a test in a Chinese stock discussion forum

Identifying domain relevant user generated content through noise reduction: a test in a Chinese stock discussion forum

Quantization of Deep Neural Networks for Accurate Edge Computing

A Multiplex Network Framework Based Recommendation Systems for Technology Intelligence

Contact Info

Product

Resources

About