<prompt> Koetlisa mokhoa oa ho ithuta oa mochini ho bona ho phoqa ho li-tweets.Ena ke pokello ea li-tweets tse tsoang ho Twitter handle sarcastic_tweets. Tweet_1: Ke letsatsi le monate hakaakang! Tweet_2: Ke thabile haholo hore ebe ke tlameha ho sebetsa ka Moqebelo Tweet_3: Katse ea ka e ntle haholo ha e robetse  <response>: Instruction in English: Train a machine learning model to detect sarcasm in tweets. . Response in English: To detect sarcasm in tweets, I would follow these steps:1. Collect and prepare data: I would gather a large dataset of tweets labeled as sarcastic or not. In addition to the tweets youve provided here, wed need many more examples for the model to accurately learn how to detect sarcasm. The data would then be pre-processed to prepare for training. This could include tokenizing the tweets breaking them into individual words or phrases, normalizing text converting all text to lowercase and removing punctuation, and removing any unnecessary stop words.2. Choose a model: There are many machine learning techniques that can be used for text classification such as Naive Bayes or Support Vector Machines SVM. Another approach could be using a neural network with an architecture specific for natural language processing tasks like LSTM or CNN. 3. Train the model: Next, I would feed the pre-processed data into the chosen machine learning model. During the training process, the model would learn to recognize patterns and correlations between the words and phrases used in sarcastic tweets versus those in non-sarcastic tweets.4. Evaluate the model: I would test the accuracy of the model using a separate set of labeled tweets. If the model accurately classifies the majority of the test tweets as either sarcastic or non-sarcastic, its likely a good model. However, if the accuracy is low, I would revisit the model selection or the training process to improve its performance.5. Making Predictions: Finally once we have an accurate model we could use it to classify new tweets from the Twitter handle sarcastic_tweets or other sources as being sarcastic or not. So, immediately I cannot tell you which tweet is sarcastic or not, because first I need a labeled data set to base my classifications. . Response in Sesotho: Ho lemoha ho phoqa ho li-tweets, ke tla latela mehato ena: 1. Bokella le ho lokisa data: Ke ne ke tla bokella dataset e kholo ea li-tweets tse ngotsoeng joalo ka ho phoqa kapa che. Ntle le li-tweets tseo u faneng ka tsona mona, re tla hloka mehlala e meng e mengata bakeng sa mohlala ho ithuta ka nepo mokhoa oa ho lemoha phoqa. Joale lintlha li ne li tla sebetsoa esale pele ho itokisetsa koetliso. Sena se ka kenyelletsa ho token li-tweets ho li arola ka mantsoe kapa lipoleloana ka bomong, ho etsa hore mongolo o tloaelehe ho fetolela mongolo o mong le o mong ho ba litlhaku tse nyane le ho tlosa matšoao a puo, le ho tlosa mantsoe a emisang ho sa hlokahale. 2. Khetha mohlala: Ho na le mekhoa e mengata ea ho ithuta ka mochini e ka sebelisoang bakeng sa ho hlophisa mongolo joalo ka Naive Bayes kapa Support Vector Machines SVM. Mokhoa o mong e ka ba ho sebelisa marang-rang a neural a nang le meralo e ikhethileng bakeng sa mesebetsi ea tsebetso ea puo ea tlhaho joalo ka LSTM kapa CNN. 3. Koetlisa mohlala: Ka mora moo, ke ne ke tla fepa data e seng e sebelitsoe ka mokhoa o khethiloeng oa ho ithuta mochine. Nakong ea thupelo, mohlala o ne o tla ithuta ho lemoha mekhoa le likamano pakeng tsa mantsoe le lipoleloana tse sebelisoang ho li-tweets tse phoqang khahlanong le li-tweets tse sa somang. 4. Hlahloba mohlala: Ke ne ke tla leka ho nepahala ha mohlala ke sebelisa sete e arohaneng ea li-tweets tse ngotsoeng. Haeba mohlala o beha ka nepo boholo ba li-tweets tsa tlhahlobo e le tse phoqang kapa tse sa phoqang, e kanna ea ba mohlala o motle. Leha ho le joalo, haeba ho nepahala ho le tlaase, ke ne ke tla khutlela ho khetho ea mohlala kapa mokhoa oa koetliso ho ntlafatsa tshebetso ea eona. 5. Ho Etsa Lipolelo-pele: Qetellong hang ha re e-na le mohlala o nepahetseng re ka o sebelisa ho arola li-tweets tse ncha ho tsoa ho Twitter handle sarcastic_tweets kapa mehloli e meng e le ea ho phoqa kapa che. Kahoo, hang-hang ha ke khone ho u joetsa hore na ke tweet efe e phoqang kapa che, hobane pele ke hloka sete ea data e ngolisitsoeng ho theha likarolo tsa ka. 