<prompt> Rala mokhoa oa ho sebetsa bakeng sa sistimi ea ho ithuta ea mochini ho sekaseka maikutlo a bareki mecheng ea litaba tsa sechaba.nan  <response>: Instruction in English: Design a workflow for a machine learning system to analyze customer sentiment on social media. . Response in English: Here is a potential workflow for a machine learning system to analyze customer sentiment on social media:1. Data Collection: The first step is to collect data from social media platforms such as Twitter, Facebook, or Instagram. This data can be in the form of posts, tweets, comments, mentions, and hashtags related to the company or its products and services.2. Data Pre-processing: The raw data collected from social media needs to be pre-processed before it can be analyzed. This may involve removing irrelevant information, such as special characters, URLs, or user mentions, and converting the text to lowercase. The data can also be tokenized into separate words, and stop words can be removed.3. Feature Extraction: The pre-processed data is then used to extract relevant features for analysis. This may involve using techniques such as bag-of-words, TF-IDF, or word embeddings to represent the text data in numerical form.4. Model Training: A machine learning model is then trained on the pre-processed data and its extracted features to classify customer sentiment as positive, negative or neutral. This can be done using algorithms such as Naive Bayes, Logistic Regression or Support Vector Machines. The model is trained on a labeled dataset where the true sentiment of each post or comment is known.5. Model Evaluation: The performance of the machine learning model is evaluated by comparing its predictions to the true sentiment labels of the data. Metrics such as accuracy, precision, recall or F1-score can be used to measure the models performance.6. Sentiment Analysis: Once the machine learning model is trained and evaluated, it can be used to analyze customer sentiment on social media. The model can be applied to new, unseen data to predict the sentiment of posts, tweets, or comments, and the results can be visualized in the form of charts, graphs, or word clouds.7. Action: The results of the sentiment analysis can be used to inform business decisions and drive customer engagement strategies. For example, if the analysis reveals a high level of negative sentiment, the company may need to address customer concerns or improve its products or services. Alternatively, if the analysis reveals a high level of positive sentiment, the company can use this to its advantage by promoting its brand or launching new marketing campaigns. . Response in Sesotho: Mona ke tsebetso e ka nang ea e-ba teng bakeng sa mokhoa oa ho ithuta oa mochine ho hlahloba maikutlo a bareki mecheng ea litaba tsa sechaba: 1. Pokello ea Boitsebiso: Mohato oa pele ke ho bokella lintlha tse tsoang mecheng ea litaba tsa sechaba tse kang Twitter, Facebook, kapa Instagram. Lintlha tsena li ka ba ka mokhoa oa li-post, li-tweets, maikutlo, litlhaloso, le li-hashtag tse amanang le khamphani kapa lihlahisoa le litšebeletso tsa eona. 2. Data Pre-processing: Lintlha tse tala tse bokelletsoeng ho tsoa mecheng ea litaba tsa sechaba li hloka ho sebetsoa pele li ka hlahlojoa. Sena se ka kenyelletsa ho tlosa lintlha tse sa lokelang, joalo ka litlhaku tse ikhethileng, li-URL, kapa mantsoe a basebelisi, le ho fetolela mongolo ho litlhaku tse nyane. Lintlha li ka boela tsa fetoloa hore e be mantsoe a arohaneng, me mantsoe a emisang a ka tlosoa. 3. Feature Extraction: Lintlha tse seng li sebelitsoe esale pele li sebelisoa ho ntša likarolo tse amehang bakeng sa tlhahlobo. Sena se ka kenyelletsa ho sebelisa mekhoa e joalo ka mokotla oa mantsoe, TF-IDF, kapa mengolo ea mantsoe ho emela lintlha tsa mongolo ka mokhoa oa linomoro. 4. Koetliso ea Mohlala: Mohlala oa ho ithuta ka mochine o koetlisoa ho data e entsoeng esale pele le likarolo tsa eona tse nkiloeng ho khetholla maikutlo a bareki e le a nepahetseng, a fosahetseng kapa a sa nke lehlakore. Sena se ka etsoa ho sebelisoa li-algorithms tse kang Naive Bayes, Logistic Regression kapa Support Vector Machines. Moetso ona o koetlisetsoa ho dataset e ngolisitsoeng moo maikutlo a nete a poso kapa maikutlo a tsejoang. 5. Tlhahlobo ea Mohlala: Tsebetso ea mohlala oa ho ithuta mochine e hlahlojoa ka ho bapisa likhakanyo tsa eona le lileibole tsa nete tsa maikutlo a data. Lintlha tse kang ho nepahala, ho nepahala, ho hopola kapa lintlha tsa F1 li ka sebelisoa ho lekanya tsebetso ea mohlala. 6. Tlhahlobo ea Maikutlo: Hang ha mokhoa oa ho ithuta oa mochine o koetlisitsoe le ho hlahlojoa, o ka sebelisoa ho hlahloba maikutlo a bareki mecheng ea litaba tsa sechaba. Mohlala o ka sebelisoa ho data e ncha, e sa bonahaleng ho bolela esale pele maikutlo a li-post, li-tweets, kapa maikutlo, me liphetho li ka bonoa ka mokhoa oa lichate, li-graph kapa maru a mantsoe. 7. Ketso: Liphetho tsa tlhahlobo ea maikutlo li ka sebelisoa ho tsebisa liqeto tsa khoebo le ho tsamaisa maano a ho kenya letsoho ha bareki. Mohlala, haeba tlhahlobo e senola boemo bo phahameng ba maikutlo a fosahetseng, khamphani e kanna ea hloka ho rarolla mathata a bareki kapa ho ntlafatsa lihlahisoa kapa litšebeletso tsa eona. Ntle le moo, haeba tlhahlobo e senola boemo bo phahameng ba maikutlo a matle, khamphani e ka sebelisa sena molemong oa eona ka ho phahamisa lebitso la eona kapa ho qala matšolo a macha a ho bapatsa. 