<prompt> Theha algorithm ea likarolo tsa mongolo ho abela polelo ka ngoe leibole. Lileibole ke positive, negative, le neutral.Ke rekile fono e ncha mme ha e sebetse.  <response>: Instruction in English: Create a text classification algorithm to assign each sentences a label. Labels are positive, negative, and neutral. . Response in English: A suitable text classification algorithm to assign each sentence a label of positive, negative, or neutral could be achieved utilizing different approaches such as logistic regression, Naive Bayes, or Support Vector Machines.One approach is explained as follows: 1. Collect and prepare data: Gather a labeled dataset with sentences and their corresponding label. Use text preprocessing methods to prepare the data, including lowercasing, tokenization, stop word removal, and stemming or lemmatization. 2. Transform text into numeric representation: Use methods such as bag-of-words or term frequency-inverse document frequency TF-IDF to convert text into numerical features.3. Train the classifier: Split the data into a training and test set. Then use a supervised machine learning algorithm, such as logistic regression or Naive Bayes, to train the classifier on the training set.4. Predict and evaluate: Use the trained classifier to predict the labels of the test set sentences. Evaluate the performance of the classifier using metrics such as accuracy, precision, and recall.For the given input sentence I bought a new phone and it doesnt work., our trained text classification algorithm could label it as negative. . Response in Sesotho: Algorithm e loketseng ea ho arola mongolo ho abela polelo ka ngoe leibole ea positive, negative, kapa neutral e ka finyelloa ho sebelisoa mekhoa e fapaneng e kang ho khutlela morao, Naive Bayes, kapa Support Vector Machines. Mokhoa o mong o hlalosoa ka tsela e latelang: 1. Bokella le ho lokisa lintlha: Bokella pokello ea data e ngolisitsoeng ka lipolelo le leibole e tsamaellanang le tsona. Sebelisa mekhoa ea ho lokisa mongolo ho lokisa datha, ho kenyelletsa litlhaku tse tlase, tokenization, ho emisa ho tlosa mantsoe, le stemming kapa lemmatization. 2. Fetolela mongolo ho ba boemeli ba linomoro: Sebelisa mekhoa e kang mokotla oa mantsoe kapa term frequency-inverse document frequency TF-IDF ho fetolela mongolo hore e be likarolo tsa linomoro. 3. Koetlisa mohlophisi: Arola lintlha ka hara sehlopha sa koetliso le tlhahlobo. Ebe u sebelisa algorithm e hlokometsoeng ea ho ithuta ka mochini, joalo ka mokhoa oa ho theola lintho kapa Naive Bayes, ho koetlisa mohlophisi ka sete ea koetliso. 4. Etsa esale pele me u hlahlobe: Sebelisa mohlahlobi ea koetlisitsoeng ho bolela esale pele lileibole tsa lipolelo tse sete ea liteko. Lekola tsebetso ea mohlopha ka ho sebelisa metrics joalo ka ho nepahala, ho nepahala, le ho hopola. Bakeng sa polelo e kentsoeng Ke rekile mohala o mocha me ha o sebetse., algorithm ea rona ea ho hlophisa mongolo o koetlisitsoeng e ka e ngola e le negative. 