<prompt> Hlalosa hore na ke hobaneng ha mehlala ea khomphutha e ka sebelisoa ha ho hlahlojoa mongolo.nan  <response>: Instruction in English: Explain why computational models can be used in analysing text. . Response in English: Computational models provide a powerful tool for analyzing text because they allow us to automate and scale the process of interpreting and extracting insights from large amounts of data. Essentially, these models use algorithms and statistical methods to identify and classify language data, uncover patterns and derive meaning. They can be applied to a wide variety of text analysis tasks, such as sentiment analysis, topic modeling, named entity recognition, and natural language processing.One key advantage of computational models is that they can handle vast amounts of data more quickly and consistently than human analysts. This is useful when dealing with large datasets, such as social media feeds, news articles, or customer reviews, where it would be impractical for humans to read and interpret each entry manually. Additionally, models can be trained to identify specific features or patterns in the data, allowing researchers to focus on particular aspects of interest.Furthermore, computational models can be used to analyze text in ways that are difficult or impossible for humans to do. For example, they can be used to uncover complex relationships and connections between words and ideas, to detect subtle shifts in tone or sentiment, and to identify and track the diffusion of topics and memes over time.Overall, computational models are an invaluable tool in text analysis, allowing researchers to efficiently process and interpret large amounts of data, uncovering insights and patterns that would otherwise be difficult or impossible to detect. . Response in Sesotho: Mehlala ea khomphutha e fana ka sesebelisoa se matla sa ho sekaseka mongolo hobane e re lumella ho iketsetsa le ho lekanya mokhoa oa ho toloka le ho ntša lintlha ho tsoa ho data e ngata. Ha e le hantle, mefuta ena e sebelisa li-algorithms le mekhoa ea lipalo-palo ho tseba le ho hlophisa lintlha tsa puo, ho senola mekhoa le ho fumana moelelo. Li ka sebelisoa mesebetsing e mengata e fapaneng ea tlhahlobo ea mongolo, joalo ka tlhahlobo ea maikutlo, mohlala oa lihlooho, kananelo ea mokhatlo, le tsebetso ea puo ea tlhaho. Molemo o mong oa bohlokoa oa mehlala ea computational ke hore e khona ho sebetsana le data e ngata kapele le ka mokhoa o tsitsitseng ho feta bahlahlobisisi ba batho. Sena se bohlokoa ha o sebetsana le li-database tse kholo, joalo ka phepelo ea litaba tsa sechaba, lingoliloeng tsa litaba, kapa maikutlo a bareki, moo ho ke keng ha khoneha hore batho ba bale le ho toloka sengoloa ka seng. Ho feta moo, mehlala e ka koetlisetsoa ho tseba likarolo kapa lipaterone tse itseng ho data, e lumellang bafuputsi ho tsepamisa maikutlo holima likarolo tse itseng tsa thahasello. Ho feta moo, mehlala ea computational e ka sebelisoa ho sekaseka mongolo ka litsela tse thata kapa tse ke keng tsa etsoa ke batho. Mohlala, li ka sebelisoa ho utolla likamano tse rarahaneng le likhokahano lipakeng tsa mantsoe le mehopolo, ho bona liphetoho tse poteletseng tsa molumo kapa maikutlo, le ho tseba le ho lekola phallo ea lihlooho le meme ha nako e ntse e ea. Ka kakaretso, mehlala ea khomphutha ke sesebelisoa sa bohlokoahali tlhahlobong ea mongolo, e lumellang bafuputsi ho sebetsa le ho hlalosa lintlha tse ngata ka nepo, ho senola lintlha le lipaterone tseo ho neng ho ka ba thata kapa ho ke keng ha khoneha ho li lemoha. 