<prompt> Etsa mohlala oa lihlopha ho fumana hore na tlhahlobo ena e ntle kapa e mpeSejo sena se ne se sa lekana chelete  <response>: Instruction in English: Create a classification model to determine whether this review is positive or negative . Response in English: Here are the steps to create a classification model that would help determine whether the given review is positive or negative:1. Data Pre-processing: transform the raw text review into numerical features that can be fed to a machine learning model. One way of doing this is by tokenizing the text, which is the process of splitting the string into individual words. With this tokenization process, common stop words can be removed, and stemming can be applied to obtain root words.2. Vectorization: After pre-processing, we need to convert the text into numerical representation. A common method is to use bag-of-words model, which counts the number of times a word appears within the review creating a sparse matrix which can be used as an input to the machine learning model.3. Training the Model: Next, many Machine Learning algorithms like Naive Bayes, logistic regression, and SVM can be used to train the model for sentiment analysis. A training dataset is necessary to train the model, where it can learn a classification function that maps the numerical features of the review to its sentiment polarity, which is either positive or negative.4. Model Evaluation: The performance of the classification model can be evaluated through various metrics such as accuracy, precision, recall, and F1-score.5. Applying the model: After training the model, it can be applied to predict the sentiment polarity of new reviews. Based on applying the above steps, the example review This dish was not worth the money would be classified as negative. . Response in Sesotho: Mehato ea ho theha mofuta oa lihlopha ke tsena o tla thusa ho tseba hore na tlhahlobo e fanoeng e nepahetse kapa e fosahetse: 1. Tsebetso ea data esale pele: fetola tlhahlobo ea mongolo e tala hore e be likarolo tsa linomoro tse ka feptjoang mokhoeng oa ho ithuta oa mochini. Tsela e ngoe ea ho etsa sena ke ka ho fetola mongolo, e leng mokhoa oa ho arola khoele hore e be mantsoe ka bomong. Ka tsebetso ena ea tokenization, mantsoe a tloaelehileng a emisang a ka tlosoa, me ho ka sebelisoa stemming ho fumana mantsoe a motso. 2. Vectorization: Ka mora ho sebetsa pele, re hloka ho fetola mongolo hore e be boemeli ba linomoro. Mokhoa o tloaelehileng ke oa ho sebelisa mohlala oa mokotla oa mantsoe, o balang palo ea makhetlo ao lentsoe le hlahang ka hara tlhahlobo ho theha matrix e fokolang e ka sebelisoang e le ho kenya letsoho ho mokhoa oa ho ithuta mochine. 3. Koetlisa Mohlala: Ka mora moo, mekhoa e mengata ea ho Ithuta Mochini e kang Naive Bayes, ho fokotseha ha lintho, le SVM e ka sebelisoa ho koetlisa mohlala bakeng sa tlhahlobo ea maikutlo. Lenane la boitsebiso la koetliso lea hlokahala ho koetlisa mohlala, moo o ka ithutang mosebetsi oa ho arola o etsang mapa oa likarolo tsa lipalo tsa tlhahlobo ho ea ho polarity ea maikutlo, e leng ntho e ntle kapa e mpe. 4. Tlhahlobo ea Mohlala: Tsebetso ea mohlala oa lihlopha e ka hlahlojoa ka metrics e sa tšoaneng e kang ho nepahala, ho nepahala, ho hopola, le F1-score. 5. Ho sebelisa mohlala: Ka mora ho koetlisa mohlala, o ka sebelisoa ho bolela esale pele maikutlo a maikutlo a litlhahlobo tse ncha. Ho itšetlehile ka ho sebelisa mehato e ka holimo, mohlala oa tlhahlobo Sejana sena se ne se sa tšoanele chelete se ne se tla hlalosoa e le se fosahetseng. 