Semantic association rule mining in text using domain ontology

Afolabi, I. T.; Sowunmi, Olaperi Yeside; Daramola, Olawande

doi:10.1504/ijmso.2017.087646

Cited by 10 publications

(10 citation statements)

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“…Therefore, they proposed LM lexicon applicable in the f inancial sector which was adopted by several studies (Das, 2014;Kearney and Liu, 2014;Gandhi et al 2019). The study of Afolabi et al (2019) was conducted in the banking context. They utilised SentiWordNet and domain ontology concept to classify bank tweets.…”

Section: Domain Based Approachesmentioning

confidence: 99%

“…The misclassified tweets were attributed to lack of Pidgin terms in NRC lexicon. To overcome this shortcoming, Afolabi et al (2019) utilised SentiWordNet and domain ontology concept to classify bank Tweets. Their study expanded Pidgin, slangs, and bank terms to develop their ontology pre-processed f ile .…”

Section: Language In Sentiment Classificationmentioning

confidence: 99%

“…Studies (Afolabi et al 2019;Oyewusi et al 2020;Ogbuju et al 2020) found have utilised English lexicon as their major lexicon to classify Pidgin text which is unsuitable based on variation of Pidgin language. Foreign language such as Pidgin English is sensitive and difficult for human to understand what some statement means.…”

Section: Summary Of Sentiment Classificationmentioning

confidence: 99%

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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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Section: Domain Based Approachesmentioning

confidence: 99%

Section: Language In Sentiment Classificationmentioning

confidence: 99%

Section: Summary Of Sentiment Classificationmentioning

confidence: 99%

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Statistical learning approaches to sentiment analysis in the Nigerian banking context

Ogunleye¹

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“…These groupings can then be counted to summarize and quantify data, which is often a laborious, time‐consuming task when done manually. Also, text mining is an effective means of finding useful information in large quantities of text data; however, a high level of accuracy cannot be attained using conventional text mining technology (Afolabi et al, 2017; Jiang et al, 2013). Of the seven area of text mining (Figure 1), this study looked to natural language processing (NLP) as the most applicable text mining (TM) technique to automate the tagging for the situation at hand.…”

Section: Literature Reviewmentioning

confidence: 99%

A smart city qualitative data analysis model: Participatory crowdsourcing of public safety reports in South Africa

Currin

Flowerday

Rey

et al. 2022

E J Info Sys Dev Countries

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Increased urbanization against the backdrop of limited resources complicates city planning and the management of functions, including public safety. The smart city concept can help, but most previous smart city systems have focused on utilizing automated sensors and analyzing quantitative data. In developing nations, limited resources make using the mobile phone to enable the crowdsourcing of qualitative public safety reports from the public a more viable option. However, there is no best practice for analyzing such citizen reports for a smart city in a developing nation.Given the rise of megacities in developing nations, many of which struggle to provide access to vital resources, this study developed and tested a model for guiding the analysis of unstructured natural language texts instead of traditional sensory data. In the study, citizens engaged with the project and 663 usable reports were received.Following a design science approach, the model was developed through an extensive review of related literature, and assessed and refined by observing the associated model prototype. This study emphasizes that a city-specific ontology needs to be developed and that natural language processing should be its focus, specifically within the larger context of our smart city qualitative data analysis (SCQDA) model.Together, these aspects enable this study to contribute practically, as we prove that cities in developing nations can improve the lives of their citizens using that which is already at their disposal instead of specialized (and often expensive) sensory networks.

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“…Manual intervention is therefore often essential. One useful solution that merits further research are domain ontologies, which help to capture the semantic context of similar words, reduce textual and noise and disambiguity (Afolabi et al 2019).…”

Section: ) Publishers Should Standardize and Gather The Information Systematicallymentioning

confidence: 99%

(My) data for (my) health – privacy calculi of terminally-ill patients with rare diseases

Dassel¹,

Klein²

2020

33rd Bled eConference – Enabling Technology for a Sustainable Society: June 28 – 29, 2020, Online Conference Proceedings

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Digital healthcare information systems promise to improve care efficiency, to reduce complexity for patients, and to increase access to information and advance research efforts. A prominent example are multi-sided-platforms (MSP), which are essentially an information business, linking key healthcare stakeholders for individualized as well as aggregated information services. However, platform-based health innovation relies on the extensive collection, storage, and use of sensitive health information, raising issues of information privacy. This study uses the privacy calculus perspective to shed light on patients’ trade-off considerations. We use the case of a MSP, which connects patients, care providers and researchers, in order to model a multi-level calculus for health information of terminally ill patients. These insights inform stepwise consent options, which highlight the trade-offs between information value and patient privacy. By reflecting on the implications for patient empowerment this conceptual paper develops a research agenda on how to study and design responsible health information systems.

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Semantic association rule mining in text using domain ontology

Cited by 10 publications

References 0 publications

Statistical learning approaches to sentiment analysis in the Nigerian banking context

Statistical learning approaches to sentiment analysis in the Nigerian banking context

A smart city qualitative data analysis model: Participatory crowdsourcing of public safety reports in South Africa

(My) data for (my) health – privacy calculi of terminally-ill patients with rare diseases

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