JobVacancyAnnouncementTextCategorizationusing MachineLearningAlgorithm

Abstract

Availabilityoflargeamountofelectronicjobvacancytextonthewebmakesthe identification ofrelevantvacancyannouncementrelated to a specifictopicisa challengingtask.It’salsotrueforAmharictexts.Amharic(ኣማርኛ)isanEthiopian languagewhichcomes from SemiticlanguageandusedasfirstlanguagebyAmhara andworkinglanguageoffederalgovernment.Largeamountofelectronictextsinthis domainhasbeengenerated.So,a textcategorizationmechanism is required for finding,filteringandmanagingtherapidgrowthofonlineinformation.Thegoalof automatictextcategorization is to classifydocumentsinto a certain numberof predefined categoriesbyusingrulebasedormachinelearning.Theaim ofthisstudy is thereforeto investigate theapplication of machine learning techniques for vacancytextcategorization. A totalof1678vacancyannouncementtextwitheightcategories:“ጤና”(health), “ምህንድስና”(engineering),“የኮምፒዉተርሳይንስዘርፎች”(computing),“ተፈጥሮሳይንስ”(natural science)“ማህበራዊሳይንስ”(socialscience),“ህግ”(law),“ግብርና”(agriculture)and“ቢዝነስ እናኢኮኖሚክስ”(businessandeconomics)werecollected.Afterpreprocessingthetextfor tokenization,stemming word variants and removing stop words and unwanted charactersandweightingtheimportanceofaterm,1610pre-categorizedtextwere usedtotraintheclassifier.Inthisstudythreesupervisedmachinelearningclassifiers, namelysupportvectormachine,kNearestNeighborandNaïveBayesclassifiersare usedtocategorizethevacancytext. Experimentalresultshowsthat,SupportVectorMachineoutperformstheothertwo classifiers(K-NearestNeighborandNaïveBayes)withanaccuracyof76.4%.Thisisa promisingresulttodesignvacancytextcategorizationmodelforjobsannouncedin Amhariclanguage.ህግ(law)categoryisanitem whichperformsthebestclassification accuracyinthecurrentstudy.Because,law categoryisanitem thatsharetheleast commontermswithotherfieldofstudywhencomparedwiththerestofanitemsused inthecurrentstudy.However,therearechallengesindesigningjobvacancytext categorizationmodel.Themainchallengeinthisstudyis;thereareconflictingtagsasa resultofcommonwordsindifferentcategorieswhereitischallengingtaskformachine xiii to categorizethesewords.Itisthereforerecommendedto applysemanticbased Amharicvacancytextcategorization.