<prompt> Etsa mokhoa oa ho beha maikutlo a lifilimi ka lihlopha tse ntle kapa tse mpenan  <response>: Instruction in English: Design a system to classify movie reviews as positive or negative . Response in English: One of the ways to automatically classify movie reviews as positive or negative is by implementing a machine learning approach known as sentiment analysis. Here are the steps to create such a system:Step 1: Data collection and preprocessingThe first step in building a movie review classifier is to collect a large dataset of movie reviews. These reviews can be obtained from movie review websites or other online platforms. The data gathered should include text reviews and their corresponding positive or negative labels. After collecting the data, preprocessing needs to be done by cleaning and organizing the text, which includes removing unnecessary characters and symbols, correcting spelling mistakes and reducing words to their base form.Step 2: Feature ExtractionOnce the text data is cleaned and preprocessed, the next step is to extract features from the text. There are several feature extraction techniques that can be used, including the bag-of-words approach and term frequency-inverse document frequency TF-IDF. These techniques convert the text data into numerical features that can be fed into a machine learning algorithm.Step 3: Model TrainingAfter preparing the data and extracting the features, a machine learning algorithm is trained on the dataset. There are various classification algorithms that can be used, such as Naive Bayes, logistic regression, support vector machines, or decision trees. The algorithm is trained on a portion of the data and its performance is validated on the remaining portion using techniques like cross-validation.Step 4: Prediction and evaluationOnce the model is trained, it can be used to predict the sentiment of new movie reviews. The models performance can be evaluated using metrics like precision, recall, F1-score, and accuracy. These metrics assess how well the model can classify new movie reviews as positive or negative. If the performance is not satisfactory, the model can be retrained and tuned for better results.Step 5: Model DeploymentAfter satisfactory results have been achieved, the final model is deployed. This can be done by integrating the model into a web or mobile application, allowing users to input movie reviews and receive an instant classification, whether it is positive or negative. The model can also be regularly updated with fresh data to maintain its accuracy. . Response in Sesotho: ngoe ea litsela tsa ho khetholla maikutlo a lifilimi ka bo eona e le a nepahetseng kapa a fosahetseng ke ka ho kenya tsebetsong mokhoa oa ho ithuta ka mochine o tsejoang e le tlhahlobo ea maikutlo. Mehato ea ho theha sistimi e joalo ke ena: Mohato oa 1: Pokello ea data le ho e lokisa esale pele Mohato oa pele oa ho aha sehlopha sa tlhahlobo ea lifilimi ke ho bokella lethathamo le leholo la litlhahlobo tsa lifilimi. Maikutlo ana a ka fumanoa liwebsaeteng tsa tlhahlobo ea lifilimi kapa liforomo tse ling tsa inthanete. Lintlha tse bokelletsoeng li tlameha ho kenyelletsa litlhahlobo tsa mongolo le lileibole tse tsamaellanang le tsona tse ntle kapa tse mpe. Ka mora ho bokella lintlha, ho lokisa esale pele ho lokela ho etsoa ka ho hloekisa le ho hlophisa mongolo, ho kenyelletsang ho tlosa litlhaku le matšoao a sa hlokahaleng, ho lokisa liphoso tsa mopeleto le ho fokotsa mantsoe ho ea ka mokhoa oa bona oa motheo. Mohato oa 2: Phatlalatso ea Feature Hang ha data ea mongolo e hloekisitsoe le ho sebetsoa esale pele, mohato o latelang ke ho ntša likarolo tsa mongolo. Ho na le mekhoa e maloa ea ho ntša likarolo tse ka sebelisoang, ho kenyeletsoa mokhoa oa mokotla oa mantsoe le frequency-inverse document frequency TF-IDF. Mekhoa ena e fetolela data ea mongolo hore e be likarolo tsa linomoro tse ka feptjoang ho algorithm ea ho ithuta mochini. Mohato oa 3: Koetliso ea Mohlala Ka mora ho lokisa lintlha le ho ntša likarolo, mokhoa oa ho ithuta ka mochine o koetlisoa setsing sa boitsebiso. Ho na le li-algorithms tse fapaneng tse ka sebelisoang, joalo ka Naive Bayes, regression ea lintho, mechini ea li-vector ea tšehetso, kapa lifate tsa liqeto. Algorithm e koetlisitsoe karolong e ngoe ea data mme tsebetso ea eona e netefatsoa karolong e setseng ho sebelisa mekhoa e kang ho netefatsa sefapano. Mohato oa 4: Bokhopo le tlhahlobo Hang ha mofuta ona o koetlisoa, o ka sebelisoa ho lepa maikutlo a maikutlo a maikutlo a libaesekopo tse ncha. Tsebetso ea mohlala e ka hlahlojoa ka ho sebelisa metrics joalo ka ho nepahala, ho hopola, lintlha tsa F1, le ho nepahala. Metrics ena e lekola hore na mohlala o ka arola litlhahlobo tsa lifilimi tse ncha e le tse ntle kapa tse mpe. Haeba tsebetso e sa khotsofatse, mohlala o ka koetlisoa hape me oa lokisoa bakeng sa liphello tse molemo. Mohato oa 5: Phatlalatso ea Moetso Ka mora hore liphetho tse khotsofatsang li fihleloe, ho tla sebelisoa mofuta oa ho qetela. Sena se ka etsoa ka ho kopanya mohlala ho web kapa sesebelisoa sa mohala, ho lumella basebelisi ho kenya maikutlo a libaesekopo le ho fumana likarolo tsa hang-hang, ebang ke tse ntle kapa tse mpe. Mohlala o ka boela oa ntlafatsoa khafetsa ka data e ncha ho boloka bonnete ba eona. 