<prompt> Ho latela lintlha tse ling, koetlisa mofuta oa tlhahlobo ea maikutlo ho bolela esale pele maikutlo.Li-tweets tse latelang mabapi le filimi e ncha: 1. Ke sa tsoa bona filimi e ncha me ke ntho e ntle ka ho fetisisa eo nkileng ka e bona! 2. Filimi e ncha e ne e le mpe - e ne e le filimi e mpe ka ho fetisisa eo nkileng ka e bona. 3. Meh, ho ne ho lokile. Ke ne nke ke ka re e ne e le khōlō kapa e tšabeha.  <response>: Instruction in English: Given some data, train a sentiment analysis model to predict sentiment. . Response in English: Based on the training data provided, we can build a sentiment analysis model to detect the sentiment of a given piece of text as being either positive, negative, or neutral. Here are the steps we can follow:1. Preprocess the data: The first step is to pre-process the text to remove any special characters, punctuation or extra spaces, and to convert the text to lower-case. We can use regular expressions to remove anything other than letters, numbers or spaces and then use the split function to tokenize the text. Here is the cleaned and tokenized version of tweet 1: i, just, saw, the, new, movie, and, it, s, the, best, thing, i, ve, ever, seen2. Label the data: The next step is to manually label the data with sentiments. The first tweet is clearly positive, second tweet is negative and third tweet is neutral. 3. Extract features: Next we extract features from the text data that can be used to train our sentiment analysis model. One simple way to do this is to create a bag-of-words model, where we keep track of the frequency of occurrence of each word in the text.4. Train the model: Once we have pre-processed and labeled the training data and extracted features from the text, the next step is to train a sentiment analysis model using an algorithm such as Naive Bayes or logistic regression. This model will learn how different words or combinations of words are associated with each of the three sentiment labels - positive, negative, or neutral.5. Predict sentiments: With the trained model, we can now predict the sentiment of any new piece of text by following similar pre-processing and feature extraction steps, and then using the model to make a prediction. . Response in Sesotho: Ho ipapisitsoe le lintlha tsa koetliso tse fanoeng, re ka theha mofuta oa tlhahlobo ea maikutlo ho bona maikutlo a sengoloa se itseng e le a nepahetseng, a fosahetseng kapa a sa nke lehlakore. Mehato eo re ka e latelang ke ena: 1. Etsa lintlha esale pele: Mohato oa pele ke ho lokisa mongolo pele ho tlosa litlhaku tse khethehileng, matšoao kapa libaka tse ling, le ho fetolela mongolo ho litlhaku tse nyane. Re ka sebelisa lipolelo tse tloaelehileng ho tlosa ntho leha e le efe ntle le litlhaku, linomoro kapa libaka ebe re sebelisa split tshebetso ho tokenize mongolo. Mofuta o hloekisitsoeng le oa tokenized oa tweet 1 ke ena: i, feela, bone, the, ncha, filimi, le, it, s, the , best, thing, i, ve, ever, see 2. Label the data: Bohato bo latelang ke ho ngola boitsebiso ka maikutlo. Tweet ea pele e hlakile ka ho hlaka, ea bobeli e mpe me ea boraro ha e nke lehlakore. 3. Likarolo tse qotsitsoeng: Ka mora moo re ntša likarolo ho data ea mongolo e ka sebelisoang ho koetlisa moetso oa rona oa tlhahlobo ea maikutlo. Tsela e ngoe e bonolo ea ho etsa sena ke ho theha mohlala oa mokotla oa mantsoe, moo re bolokang tlaleho ea hore lentsoe ka leng le hlaha hangata hakae temaneng. 4. Koetlisa mohlala: Hang ha re se re hlophisitse pele le ho ngola lintlha tsa koetliso le likarolo tse nkiloeng ho tsoa sengolong, mohato o latelang ke ho koetlisa mohlala oa ho hlahloba maikutlo ho sebelisa algorithm e kang Naive Bayes kapa logistic regression. Moetso ona o tla ithuta hore na mantsoe a fapaneng kapa motsoako oa mantsoe o amahanngoa joang le lengolo le leng le le leng la a mararo a maikutlo - a nepahetseng, a fosahetseng kapa a sa nke lehlakore. 5. Nehelana ka maikutlo: Ka mmotlolo o koetlisitsoeng, joale re ka noha maikutlo a sengoathoana sefe kapa sefe se secha ka ho latela mehato e tsoanang ea tsebetso le likarolo tsa ho hula, ebe re sebelisa mohlala ho etsa ponelopele. 