<prompt> Fana ka mokhoa oa ho hlophisa li-tweets ho latela maikutlo.nan  <response>: Instruction in English: Provide a way to classify tweets according to the sentiment. . Response in English: One way to classify tweets according to sentiment is to use a natural language processing algorithm. This involves training a machine learning model on a large dataset of pre-labeled tweets that have been categorized according to sentiment e.g. positive, negative, neutral. Once the model is trained, it can then analyze and categorize new tweets based on the likelihood of them expressing a specific sentiment.Here are the steps to classify tweets according to sentiment:1. Collect and preprocess data: Gather a large dataset of tweets along with their corresponding sentiment labels. Preprocessing the data involves cleaning and preparing the text for analysis, which may include removing irrelevant information, normalizing text, and tokenizing it.2. Train the model: Using the pre-labeled dataset, train a machine learning classifier to recognize patterns and keywords that are associated with different sentiments. There are several algorithms that can be used for this purpose, including Naive Bayes, Support Vector Machines, and Random Forests.3. Test the model: Test the trained model on a set of tweets to evaluate its performance. This can be done by splitting the dataset into a training and testing set, or by using cross-validation techniques.4. Classify new tweets: Once the model is trained and tested, it can be used to classify new tweets into different sentiment categories. The algorithm will analyze the text and assign a sentiment label based on its learned associations between words and sentiments.In summary, sentiment analysis of tweets can be done by training a machine learning model on pre-labeled data, and then using the trained model to classify new tweets according to their sentiment. . Response in Sesotho: Mokhoa o mong oa ho arola li-tweets ho latela maikutlo ke ho sebelisa algorithm ea puo ea tlhaho. Sena se kenyelletsa ho koetlisa mohlala oa ho ithuta ka mochini ho pokello ea data e kholo ea li-tweets tse ngoliloeng esale pele tse arotsoeng ho latela maikutlo mohlala, a nepahetseng, a fosahetseng, a sa nke lehlakore. Hang ha mohlala o se o koetlisitsoe, o ka sekaseka le ho arola li-tweets tse ncha ho latela monyetla oa hore li hlahise maikutlo a itseng. Mehato ea ho hlopha li-tweets ho ea ka maikutlo ke ena: 1. Bokella me u lokise data esale pele: Bokella pokello ea data e kholo ea li-tweets hammoho le mangolo a bona a maikutlo. Ho lokisa lintlha esale pele ho kenyelletsa ho hloekisa le ho lokisa mongolo bakeng sa tlhahlobo, ho ka kenyelletsang ho tlosa tlhahisoleseling e sa lokelang, ho etsa hore mongolo o tloaelehe, le ho o etsa tokening. 2. Koetlisa mohlala: U sebelisa pokello ea boitsebiso e ngolisitsoeng pele, koetlisa mohlophisi oa ho ithuta oa mochine ho lemoha mekhoa le mantsoe a bohlokoa a amanang le maikutlo a fapaneng. Ho na le li-algorithms tse maloa tse ka sebelisetsoang morero ona, ho kenyeletsoa Naive Bayes, Mechini ea Tsehetso ea Vector, le Random Forests. 3. Lekola mohlala: Lekola mohlala o koetlisitsoeng ka sete ea li-tweets ho lekola tšebetso ea ona. Sena se ka etsoa ka ho arola dataset hore e be sete ea koetliso le liteko, kapa ka ho sebelisa mekhoa e fapaneng ea netefatso. 4. Hlophisa li-tweets tse ncha: Hang ha mohlala o se o koetlisoa le ho lekoa, o ka sebelisoa ho arola li-tweets tse ncha ka lihlopha tse fapaneng tsa maikutlo. Algorithm e tla sekaseka sengoloa mme e fane ka lengolo la maikutlo ho latela likamano tsa eona tse ithutoang lipakeng tsa mantsoe le maikutlo. Ka bokhutšoanyane, tlhahlobo ea maikutlo ea li-tweets e ka etsoa ka ho koetlisa mohlala oa ho ithuta ka mochini ho data e ngolisitsoeng pele, ebe o sebelisa mohlala o koetlisitsoeng ho hlophisa li-tweets tse ncha ho latela maikutlo a bona. 