Twitter mining for ontology-based domain discovery incorporating machine learning

Abstract

Purpose This paper aims to obtain the domain of the textual content generated by users of online social network (OSN) platforms. Understanding a users’ domain (s) of interest is a significant step towards addressing their domain-based trustworthiness through an accurate understanding of their content in their OSNs. Design/methodology/approach This study uses a Twitter mining approach for domain-based classification of users and their textual content. The proposed approach incorporates machine learning modules. The approach comprises two analysis phases: the time-aware semantic analysis of users’ historical content incorporating five commonly used machine learning classifiers. This framework classifies users into two main categories: politics-related and non-politics-related categories. In the second stage, the likelihood predictions obtained in the first phase will be used to predict the domain of future users’ tweets. Findings Experiments have been conducted to validate the mechanism proposed in the study framework, further supported by the excellent performance of the harnessed evaluation metrics. The experiments conducted verify the applicability of the framework to an effective domain-based classification for Twitter users and their content, as evident in the outstanding results of several performance evaluation metrics. Research limitations/implications This study is limited to an on/off domain classification for content of OSNs. Hence, we have selected a politics domain because of Twitter’s popularity as an opulent source of political deliberations. Such data abundance facilitates data aggregation and improves the results of the data analysis. Furthermore, the currently implemented machine learning approaches assume that uncertainty and incompleteness do not affect the accuracy of the Twitter classification. In fact, data uncertainty and incompleteness may exist. In the future, the authors will formulate the data uncertainty and incompleteness into fuzzy numbers which can be used to address imprecise, uncertain and vague data. Practical implications This study proposes a practical framework comprising significant implications for a variety of business-related applications, such as the voice of customer/voice of market, recommendation systems, the discovery of domain-based influencers and opinion mining through tracking and simulation. In particular, the factual grasp of the domains of interest extracted at the user level or post level enhances the customer-to-business engagement. This contributes to an accurate analysis of customer reviews and opinions to improve brand loyalty, customer service, etc. Originality/value This paper fills a gap in the existing literature by presenting a consolidated framework for Twitter mining that aims to uncover the deficiency of the current state-of-the-art approaches to topic distillation and domain discovery. The overall approach is promising in the fortification of Twitter mining towards a better understanding of users’ domains of interest.

Unlocking Social Media and User Generated Content as a Data Source for Knowledge Management

“…In this context, a thread of efforts has steered towards extracting knowledge from UGC to inform actionable intelligence (Abu-Salih, Wongthongtham, & Chan, 2018;Hultgren, Jennex, Persano,&Ornatowski,2016).Asnoted,OSNshavealreadybeenextensivelyusedasapowerful tooltopromoteknowledgeextractionandmanagementinseveraldomains (Arularasan,Suresh,& Seerangan,2018;Kasemsap,2019;Nishikant,PrabinKumar,&ShashiKant,2018).Givensuchan impact,understandingwaystobroadenthisscopeandextradatafromnewsourcessuchasUGCisof 104 interesttomanypractitionersandresearchersalike.Infact,eventhoughidentifying,reviewing,and interpretingsocialcontentconsumessubstantialtimeandeffort (Chang,Diaz,&Hung,2015),itstill attractswideinterestduetothepotentialtoapplyKMtoobtainhighqualitycontent,andactionable intelligenceinmanydisciplinesincludingpolitics (Cruz,2019),e-commerce(Schaupp&Bélanger, 2019),e-learning (Hosseingholizadeh,Sharif,&Kouhsari,2018),andhealthcare (Surendro,Satya, &Yodihartomo,2018).Othereffortshaveprovidedunconventionalandadvancedperceptionsto framethisconstantgrowthofUGC,alongsideotherBigDataislands.Forexample, Jennex(2017) presentedarevisedversionofthetraditionalKMpyramidthatincorporatesBigData,Internetof Things(IoT)andBusinessIntelligence(BI)toprovideanoverarchingparadigmtowardbetterdecision supportstrategies.…”

Predicting River Pollution Using Random Forest Decision Tree with GIS Model: A Case Study of MMORS, Philippines

“…Random Forest Decision Tree classification as the major learning algorithm implemented in this undertaking is further utilized as a training data and test results to predict the MMORS river condition with its corresponding water pollution level classification indicated as -Excellent‖, -Good‖, -Poor‖, -Very Poor, and -Worst‖ This section describes the different metrics used by the researcher in evaluating the classifier model performance [8]; its effectiveness and the quality of its prediction. Several tests of data with known water quality parameter values are used to test the accuracy of the generated sample by distinguishing the reliability of the data and their validity in accordance to the comparison of an observed accuracy with an expected accuracy rate that is likely to meet based on the Confusion Matrix [9]. The classifier can also be evaluated in terms of Precision, Recall, and F-measure and the assessment of interrater-reliability [10] .Cohen's Kappa is used which is shown in Table IV.…”

Inferring the votes in a new political landscape. The case of the 2019 Spanish Presidential elections.

“…It is also directly linked to the capability to discard user's messages that are classi ed as spam i.e. unsolicited and repeated junk messages (Abu-Salih et al, 2020;Abu-Salih et al, 2018;Abu-Salih, et al, 2019b;. These tweets come usually from bots and have a malicious intention to create rumours and chaos (Shin et al, 2017).…”

“…A SA system is highly sensitive to the domain in which the data used to train are extracted. It can obtain poor results if the training dataset is not political (Abu-Salih et al, 2018;. Due to the lack of a sentiment lexicon for non-English languages, the creation of a new polarity lexicon is decided for the Spanish political event issuing from two different sources.…”