<prompt> Rala mofuta oa sehlopha bakeng sa tlhahlobo ea maikutlo ea maikutlo a libaesekopo.nan  <response>: Instruction in English: Design a classification model for sentiment analysis of movie reviews. . Response in English: Sentiment analysis is the task of classifying text into different categories based on the sentiment expressed in the text such as positive, negative or neutral. Here is an outline of how to design a classification model for sentiment analysis of movie reviews:1. Data collection and preprocessing: The first step is to collect and preprocess movie reviews data. This can be done by scraping movie review websites, or using publicly available datasets of movie reviews. Preprocessing steps include removing stop words, punctuations, and special characters, and tokenizing the text.2. Feature Extraction: Once the data is preprocessed, the next step is to extract relevant features from the text data. One common approach used in sentiment analysis is to represent the text as a bag of words or a TF-IDF vector. Other approaches include using word embeddings e.g., Word2Vec, GloVe to represent the text as dense vectors.3. Model Selection: Once the features are extracted, the next step is to choose a suitable classification model. Commonly used models for text classification include Naïve Bayes, Logistic Regression, Support Vector Machines, and Random Forests, among others. Deep learning models such as Convolutional Neural Networks CNN and Recurrent Neural Networks RNN can also be used, particularly for large datasets.4. Model Training and Evaluation: Once the model is selected, it is trained on the preprocessed and feature-extracted text data using standard machine learning techniques. The trained model is then evaluated on a hold-out dataset to assess its performance. Evaluation metrics commonly used for classification tasks include accuracy, precision, recall, and F1 score.5. Fine-tuning: Based on the results of the evaluation, the model may be fine-tuned to improve its performance. This can include steps like hyperparameter tuning, adding more training data, and trying different feature extraction techniques or model architectures.In conclusion, designing a classification model for sentiment analysis of movie reviews involves several steps including data collection and preprocessing, feature extraction, model selection, training, and evaluation. Once the model is trained and evaluated, it can be fine-tuned to improve its performance. . Response in Sesotho: Tshekatsheko ya maikutlo ke mosebetsi wa ho arola sengolwa ka dikarolo tse fapaneng ho itshetlehile ka maikutlo a hlahiswang sengolweng jwalo ka a nepahetseng, a fosahetseng kapa a sa nke lehlakore. Mona ke kemiso ea mokhoa oa ho rala mohlala oa lihlopha bakeng sa tlhahlobo ea maikutlo ea maikutlo a litlhahlobo tsa libaesekopo: 1. Pokello ea data le ho e lokisa esale pele: Mohato oa pele ke ho bokella le ho lokisa pele lintlha tsa litlhahlobo tsa lifilimi. Sena se ka etsoa ka ho hlakola liwebsaete tsa tlhahlobo ea lifilimi, kapa ho sebelisa li-database tse fumanehang phatlalatsa tsa maikutlo a lifilimi. Mehato ea ho lokisa esale pele e kenyelletsa ho tlosa mantsoe a emisang, matšoao a puo le litlhaku tse ikhethileng, le ho etsa tokening mongolo. 2. Feature Extraction: Hang ha data e se e hlophisitsoe, mohato o latelang ke ho ntša lintlha tse amehang ho data ea mongolo. Mokhoa o mong o tloaelehileng o sebelisoang tlhahlobong ea maikutlo ke ho emela sengoloa joalo ka mokotla oa mantsoe kapa vector ea TF-IDF. Mekhoa e meng e kenyelletsa ho sebelisa mantsoe a kenyelletsang mantsoe mohlala, Word2Vec, GloVe ho emela mongolo joalo ka li-vector tse teteaneng. 3. Khetho ea Mohlala: Hang ha likarolo li ntšitsoe, mohato o latelang ke ho khetha mofuta o loketseng oa lihlopha. Mefuta e sebelisoang hangata bakeng sa ho hlophisa mongolo e kenyelletsa Naïve Bayes, Logistic Regression, Support Vector Machines, le Random Forests, hara tse ling. Mehlala ea ho ithuta e tebileng joalo ka Convolutional Neural Networks CNN le Recurrent Neural Networks RNN le eona e ka sebelisoa, haholo bakeng sa li-database tse kholo. 4. Koetliso le Tlhahlobo ea Mohlala: Hang ha mohlala o khethiloe, o koetlisetsoa ho latela lintlha tsa mongolo tse fetisitsoeng le tse ntšitsoeng ho sebelisoa mekhoa e tloaelehileng ea ho ithuta ka mochine. Ka mora moo, mohlala o koetlisitsoeng o hlahlobjoa ka hara pokello ea data ho lekola tšebetso ea ona. Melemo ea tlhahlobo e atisang ho sebelisoa bakeng sa mesebetsi ea lihlopha e kenyelletsa ho nepahala, ho nepahala, ho hopola, le lintlha tsa F1. 5. Tokiso e ntle: Ho ipapisitsoe le liphetho tsa tlhahlobo, mohlala o ka hlophisoa hantle ho ntlafatsa tsebetso ea ona. Sena se ka kenyelletsa mehato e kang hyperparameter tuning, ho eketsa lintlha tsa koetliso, le ho leka mekhoa e fapaneng ea ho ntša likarolo kapa mekhoa ea ho haha. Qetellong, ho rala mofuta oa likarolo bakeng sa tlhahlobo ea maikutlo ea litlhahlobo tsa lifilimi ho kenyelletsa mehato e mengata ho kenyelletsa pokello ea data le ho e lokisa esale pele, tlhahiso ea likarolo, khetho ea mohlala, koetliso le tlhahlobo. Hang ha mohlala o koetlisitsoe le ho hlahlojoa, o ka lokisoa hantle ho ntlafatsa tsebetso ea oona. 