Rule-Based Topic Trend Analysis by Using Data Mining Techniques

Jeon, Yunwan; Cho, Chanho; Seo, Jongwoo; Kwon, Kyunglag; Park, Han-Saem; Chung, In-Jeong

doi:10.1007/978-981-10-5041-1_75

Cited by 5 publications

(7 citation statements)

References 9 publications

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“…Generally, after data is being collected, the noise in the text needs to be cleaned using Natural Language Processing (NLP) process. The detected text is then documented into matrices forms using the Latent Dirichlet Allocation (LDA) algorithm [6].…”

Section: A On-line-analytical Processing and Association Rule Miningmentioning

confidence: 99%

“…Park and his team (2017) used OLAP and ARM to analyse the social trend and identify similar discussion topics from different users and insights. They showed the feasibility of a combination of the two different data mining techniques [6]. However, challenges still remained for a better understanding of topic trends.…”

Section: A On-line-analytical Processing and Association Rule Miningmentioning

confidence: 99%

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Predicting Book Sales Trend using Deep Learning Framework

Feng¹,

Choy²,

Laik³

2020

IJACSA

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A deep learning framework like Generative Adversarial Network (GAN) has gained popularity in recent years for handling many different computer visions related problems. In this research, instead of focusing on generating the near-real images using GAN, the aim is to develop a comprehensive GAN framework for book sales ranks prediction, based on the historical sales rankings and different attributes collected from the Amazon site. Different analysis stages have been conducted in the research. In this research, a comprehensive data preprocessing is required before the modeling and evaluation. Extensive predevelopment on the data, related features selections for predicting the sales rankings, and several data transformation techniques are being applied before generating the models. Later then various models are being trained and evaluated on prediction results. In the GAN architecture, the generator network that used to generate the features is being built, and the discriminator network that used to differentiate between real and fake features is being trained before the predictions. Lastly, the regression GAN model prediction results are compared against the different neural network models like multilayer perceptron, deep belief network, convolution neural network.

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Section: A On-line-analytical Processing and Association Rule Miningmentioning

confidence: 99%

Section: A On-line-analytical Processing and Association Rule Miningmentioning

confidence: 99%

Predicting Book Sales Trend using Deep Learning Framework

Feng¹,

Choy²,

Laik³

2020

IJACSA

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“…The star schema resembles a starburst with the dimension tables shown in an outspread pattern around the central fact table. It is defined by dimensions and facts: (1) dimensions mean the perspectives and objects with respect to what a user hopes to keep records; and (2) facts include the names of the facts or measures as well as the keys to each of the related dimension tables [26,27].…”

Section: Multidimensional Data Cubementioning

confidence: 99%

In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining

et al. 2017

Self Cite

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Significant amounts of energy are consumed in the commercial building sector, resulting in various adverse environmental issues. To reduce energy consumption and improve energy efficiency in commercial buildings, it is necessary to develop effective methods for analyzing building energy use. In this study, we propose a data cube model combined with association rule mining for more flexible and detailed analysis of building energy consumption profiles using the Commercial Buildings Energy Consumption Survey (CBECS) dataset, which has accumulated over 6700 existing commercial buildings across the U.S.A. Based on the data cube model, a multidimensional commercial sector building energy analysis was performed based upon on-line analytical processing (OLAP) operations to assess the energy efficiency according to building factors with various levels of abstraction. Furthermore, the proposed analysis system provided useful information that represented a set of energy efficient combinations by applying the association rule mining method. We validated the feasibility and applicability of the proposed analysis model by structuring a building energy analysis system and applying it to different building types, weather conditions, composite materials, and heating/cooling systems of the multitude of commercial buildings classified in the CBECS dataset.

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“…Detecting places is not a trivial task, and major challenges associated with tweets must be addressed, such as the ungrammatical nature of tweets, as well as informal abbreviations and lexicons (for example, mentioning a location using a hashtag). With respect to temporal information, we cluster values of the time and duration of tweets connected to the event of interest by similarity within a window of time [28]. To capture the semantic, morphological, and contextual richness of each word in a tweet, we perform a word-level analysis by using Word Embeddings [29,30], a widely used algorithm that transforms similar words into a continuous vector space.…”

Section: Introductionmentioning

confidence: 99%

Using Twitter Data to Monitor Natural Disaster Social Dynamics: A Recurrent Neural Network Approach with Word Embeddings and Kernel Density Estimation

Hernandez-Suarez

Sánchez-Pérez

Toscano-Medina

et al. 2019

Sensors

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In recent years, Online Social Networks (OSNs) have received a great deal of attention for their potential use in the spatial and temporal modeling of events owing to the information that can be extracted from these platforms. Within this context, one of the most latent applications is the monitoring of natural disasters. Vital information posted by OSN users can contribute to relief efforts during and after a catastrophe. Although it is possible to retrieve data from OSNs using embedded geographic information provided by GPS systems, this feature is disabled by default in most cases. An alternative solution is to geoparse specific locations using language models based on Named Entity Recognition (NER) techniques. In this work, a sensor that uses Twitter is proposed to monitor natural disasters. The approach is intended to sense data by detecting toponyms (named places written within the text) in tweets with event-related information, e.g., a collapsed building on a specific avenue or the location at which a person was last seen. The proposed approach is carried out by transforming tokenized tweets into word embeddings: a rich linguistic and contextual vector representation of textual corpora. Pre-labeled word embeddings are employed to train a Recurrent Neural Network variant, known as a Bidirectional Long Short-Term Memory (biLSTM) network, that is capable of dealing with sequential data by analyzing information in both directions of a word (past and future entries). Moreover, a Conditional Random Field (CRF) output layer, which aims to maximize the transition from one NER tag to another, is used to increase the classification accuracy. The resulting labeled words are joined to coherently form a toponym, which is geocoded and scored by a Kernel Density Estimation function. At the end of the process, the scored data are presented graphically to depict areas in which the majority of tweets reporting topics related to a natural disaster are concentrated. A case study on Mexico’s 2017 Earthquake is presented, and the data extracted during and after the event are reported.

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Rule-Based Topic Trend Analysis by Using Data Mining Techniques

Cited by 5 publications

References 9 publications

Predicting Book Sales Trend using Deep Learning Framework

Predicting Book Sales Trend using Deep Learning Framework

In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining

Using Twitter Data to Monitor Natural Disaster Social Dynamics: A Recurrent Neural Network Approach with Word Embeddings and Kernel Density Estimation

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