<prompt> Hlalosa hore na tlhahlobo ea maikutlo e sebetsa joang.nan  <response>: Instruction in English: Explain how sentiment analysis works. . Response in English: Sentiment analysis, also known as opinion mining, is the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the authors attitude towards a particular topic, product or service. Sentiment analysis uses various natural language processing techniques and machine learning algorithms to analyze text and identify not only the polarity of sentiment positive, negative or neutral but also its intensity, subjectivity and emotional states. Sentiment analysis can be performed on different levels such as word, sentence or document level.The most common approach to sentiment analysis is the use of a pre-existing sentiment lexicon or dictionary that contains lists of words associated with positive, negative or neutral emotions. The words in the text are then matched with the words in the sentiment lexicon and a score is computed based on the number of positive and negative words. The text is then classified as positive, negative, or neutral based on the resulting score. More advanced methods employ machine learning techniques to train a classifier on a labeled dataset that can then be used to classify new texts.Aside from lexicon-based and machine learning methods, sentiment analysis can also utilize rule-based approaches, where rules are defined using a set of linguistic and heuristic criteria, and hybrid methods which combine the strengths of the previously mentioned approaches.Sentiment analysis has many practical applications, including social media monitoring, brand reputation management, customer service, and market research. . Response in Sesotho: Tlhahlobo ea maikutlo, e tsejoang hape e le merafo ea maikutlo, ke mokhoa oa ho hloaea maikutlo le ho arola ka lihlopha maikutlo a hlahisoang karolong ea mongolo, haholo-holo ho fumana hore na maikutlo a mongoli mabapi le sehlooho se itseng, sehlahisoa kapa tšebeletso e itseng. Tlhahlobo ea maikutlo e sebelisa mekhoa e fapaneng ea ho sebetsana le puo ea tlhaho le li-algorithms tsa ho ithuta ka mochini ho sekaseka sengoloa le ho supa eseng feela polane ea maikutlo e ntle, e mpe kapa e sa nke lehlakore empa hape le botebo ba eona, boikemelo le maemo a maikutlo. Tlhahlobo ea maikutlo e ka etsoa maemong a fapaneng joalo ka mantsoe, polelo kapa boemo ba litokomane. Mokhoa o atileng haholo oa tlhahlobo ea maikutlo ke tšebeliso ea lexicon ea maikutlo e seng e le teng kapa buka e hlalosang mantsoe e nang le lethathamo la mantsoe a amanang le maikutlo a nepahetseng, a fosahetseng kapa a sa nke lehlakore. Joale mantsoe a mongolong a nyallana le mantsoe a lexicon ea maikutlo me lintlha li baloa ho ipapisitsoe le palo ea mantsoe a nepahetseng le a fosahetseng. Sengoloa joale se hlophisoa e le se nepahetseng, se fosahetseng, kapa se sa nke lehlakore ho latela lintlha tse hlahisitsoeng. Mekhoa e tsoetseng pele haholo e sebelisa mekhoa ea ho ithuta ka mochini ho koetlisa motho ea khethollang sehlopha sa data se ngotsoeng se ka sebelisoang ho hlophisa litemana tse ncha. Ntle le mekhoa ea ho ithuta e thehiloeng ho li-lexicon le mochine, tlhahlobo ea maikutlo e ka boela ea sebelisa mekhoa e itšetlehileng ka melao, moo melao e hlalosoang ho sebelisoa mekhoa ea lipuo le li-heuristic, le mekhoa e nyalisitsoeng e kopanyang matla a mekhoa e boletsoeng pejana. Tlhahlobo ea maikutlo e na le litsebetso tse ngata tse sebetsang, ho kenyeletsoa tlhahlobo ea mecha ea litaba ea sechaba, taolo ea botumo ba lebitso, litšebeletso tsa bareki le lipatlisiso tsa mebaraka. 