Social Network Data Mining Using Natural Language Processing and Density Based Clustering

Khanaferov, David; Luc, Christopher; Wang, Taehyung

doi:10.1109/icsc.2014.48

Cited by 15 publications

(4 citation statements)

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“…Methods used in geograhic identification are typically based on unsupervised learning. For example, DBSCAN clustering was used to monitor and track obesity levels within the population [54], as well as track the spread of the dengue virus [21]. Another study utilized hot spot analysis to examine spatial patterns of depression on Twitter.…”

Section: Geographic Identificationmentioning

confidence: 99%

A scoping review of the use of Twitter for public health research

Edo-Osagie

Iglesia

Lake

et al. 2020

Computers in Biology and Medicine

114

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Public health practitioners and researchers have used traditional medical databases to study and understand public health for a long time. Recently, social media data, particularly Twitter, has seen some use for public health purposes. Every large technological development in history has had an impact on the behaviour of society. The advent of the internet and social media is no different. Social media creates public streams of communication, and scientists are starting to understand that such data can provide some level of access into the people's opinions and situations. As such, this paper aims to review and synthesize the literature on Twitter applications for public health, highlighting current research and products in practice. A scoping review methodology was employed and four leading health, computer science and cross-disciplinary databases were searched. A total of 755 articles were retreived, 92 of which met the criteria for review. From the reviewed literature, six domains for the application of Twitter to public health were identified: (i) Surveillance; (ii) Event Detection; (iii) Pharmacovigilance; (iv) Forecasting; (v) Disease Tracking; and (vi) Geographic Identification. From our review, we were able to obtain a clear picture of the use of Twitter for public health. We gained insights into interesting observations such as how the popularity of different domains changed with time, the diseases and conditions studied and the different approaches to understanding each disease, which algorithms and techniques were popular with each domain, and more.

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Section: Geographic Identificationmentioning

confidence: 99%

A scoping review of the use of Twitter for public health research

Edo-Osagie

Iglesia

Lake

et al. 2020

Computers in Biology and Medicine

114

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“…The learning algorithm uncovers pattern from unlabelled textual data. Examples are the clustering models such as k-means clustering (Kumari & Babu, 2016), Density-Based Spatial Clustering of Applications with Noise (Khanaferov et al 2014) and fuzzy clustering (Suresh, 2016). The pattern or structure aids in classifying the sentiment of a topic or subject.…”

Section: Unsupervised Machine Learningmentioning

confidence: 99%

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Ogunleye¹

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Banking is an essential component of our day-to-day activity, and the sector contributes to the development of every Nation's economy. To ensure bank stability, the banks have gone through several deregulation and reformation processes. Unfortunately, these processes and technological advancement have led to increased competitiveness, saturated market, and low profitability. Thus, the need for banks to build customer centric service to gain profitability and stability is vital. This opens the need to investigate customers attitude towards banking.With the growing usage of social network sites (SNS), the user generated content (UGC) has opened opportunities for banks to mine customers opinion . This can help the banks to generate insight into their product and services, create marketing strategies and manage their reputation. To the customers, this serves as source to information that can help in supporting decision making on product or service purchase. In this study, sentiment analysis (SA) techniques were employed to investigate customers attitude towards banking us ing Twitter data. However, the unstructured nature of the data and word ambiguity made sentiment analysis complicated. In the context of this study, SA is more difficult because it involves the natural language processing of Pidgin English and English words in the bank domain.Unfortunately, there are limited or no resources for this purpose.

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“…For the data mining research, Kahanaferov et al [2] first collected unstructured data from Twitter. Their goal was to demonstrate a practical approach to solve an alarming healthcare issue through a computational approach centered on mining useful patterns out of public data.…”

Section: Related Workmentioning

confidence: 99%

Investigation of localized sentiment for a given product by analyzing tweets

Hridoy

Ekram

Islam

et al. 2015

2015 IEEE/ACIS 14th International Conference on Computer and Information Science (ICIS)

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Big data analysis has a huge importance not only to students and researchers but also to small, medium and large businesses. This is mainly due to the fact that big data is almost abundant storage of critical information. Any kind of products and services that people enj oy or do not find very helpful, at least a handful of people around the world will always post about it on social media. In this paper, we have discussed a proper methodology for the utilization and interpretation of twitter data which lead to some interesting insights into the world of public opinions about the iPhone 6. We also included various feature popularities and male-and-female reactions to the product. We found mixed opinions, most of which were consistent with the general comments and opinions expressed by users.

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Social Network Data Mining Using Natural Language Processing and Density Based Clustering

Cited by 15 publications

References 0 publications

A scoping review of the use of Twitter for public health research

A scoping review of the use of Twitter for public health research

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Investigation of localized sentiment for a given product by analyzing tweets

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