Research Article Sentence Classification Using N-Grams in Urdu Language Text Malik Daler Ali Awan , 1 Sikandar Ali , 2 Ali Samad , 1 Nadeem Iqbal , 3 Malik Muhammad Saad Missen , 1 and Niamat Ullah 4 1 Department of Information Technology, Faculty of Computing, e Islamia University of Bahawalpur, 63100 Bahawalpur, Pakistan 2 Department of Information Technology, e University of Haripur, 22621 Haripur, Khyber Pakhtunkhwa, Pakistan 3 Muhammad Nawaz Shareef University of Agriculture, Multan 61000, Pakistan 4 Department of Computer Science, University of Buner, 19290 Sawarai Buner, Khyber Pakhtunkhwa, Pakistan CorrespondenceshouldbeaddressedtoSikandarAli;sikandar@cup.edu.cn Received 17 April 2021; Revised 27 May 2021; Accepted 7 November 2021; Published 22 November 2021 AcademicEditor:Wei-ChuenYau Copyright©2021MalikDalerAliAwanetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. eusageoflocallanguagesisbeingcommoninsocialmediaandnewschannels.epeoplesharetheworthyinsightsabout various topics related to their lives in different languages. A bulk of text in various local languages exists on the Internet that containsinvaluableinformation.eanalysisofsuchtypeofstuff(locallanguage’stext)willcertainlyhelpimproveanumberof Natural Language Processing (NLP) tasks. e information extracted from local languages can be used to develop various applicationstoaddnewmilestoneinthefieldofNLP.Inthispaper,wepresentedanappliedresearchtask,“multiclasssentence classificationforUrdulanguagetextatsentencelevelexistingonthesocialnetworks,i.e.,Twitter,Facebook,andnewschannelsby usingN-gramsfeatures.”Ourdatasetconsistsofmorethan1,00000instancesoftwelve(12)differenttypesoftopics.Afamous machinelearningclassifierRandomForestisusedtoclassifythesentences.Itshowed80.15%,76.88%,and64.41%accuracyfor unigram, bigram, and trigram features, respectively. 1. Introduction e text is still dominant and prominent way of commu- nication instead of only pictures, emoji, sounds, and ani- mations. e innovative environment of communication, thereal-timeavailabilityoftheInternet,andtheunrestricted communicationmodeofsocialnetworksattractedbillionsof peoplearoundtheworld.Peopleshareinsightsaboutvarious topics,opinions,views,ideas,andeventshappeningaround themonsocialnetworksindifferentlanguages.Socialmedia andnewschannels:suchcommunicationplatformscreated spaceforlocallanguagestoshareinformation.Googleinput tool (https://www.google.com/inputtools/) provides the language transliteration support to 88 different languages. edevelopmentofmanylocallanguagessupportingtoolsis anotherfactorthatboostedtheusageoflocallanguageson socialmediaandnewschannels.Obviously,peoplepreferto communicateinlocallanguagesinsteadofgloballanguages becauseofeasinessinconveyingmessages.Itisalsocausing to generate heterogeneous data on Internet. Sifting worthy insights from an immense amount of heterogeneous text of multiple local languages existing on socialmediaisoneoftheinterestingandchallengingtasksof Natural Language Processing (NLP). Local language pro- cessingcertainlyprovidestheinvaluableinsightstodevelop NLP applications. ese applications can respond in emergencies, outbreaks, and natural disasters, i.e., rain, flood, and earthquake [1]. e interesting feature like real- time interaction of social media has facilitated millions of people to share their intent, appreciation, or criticism [2], i.e., enjoying discount offer by selling brands or criticizing the quality of the product. Extracting and classifying such information are valuable to improve the quality of the product.eimplementationofsmartcitiespossessesalot ofchallenges,suchasdecisionmaking,eventmanagement, communication,andinformationretrieval.Extractinguseful Hindawi Scientific Programming Volume 2021, Article ID 1296076, 11 pages https://doi.org/10.1155/2021/1296076