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Spam detection aims to identify and filter out unwanted emails or messages. Common approaches include Naive Bayes classifiers, Support Vector Machines (SVM), and neural networks, employing features like message content, sender info, and frequency of certain words.

Language identification is about determining the language that a given piece of text is written in. Common approaches include n-gram models, CNNs, and RNNs, often utilizing character-level features and language profiles based on frequency of certain character sequences.

Sentiment analysis determines the emotional tone behind a body of text. This is a common text classification problem where machine learning models like Logistic Regression, LSTM networks, and transformers (like BERT) are used to classify text as positive, negative, or neutral.

News categorization assigns news articles to predefined categories, such as politics, sports, or finance. Common methods used are Naive Bayes, Random Forests, and neural networks. The use of NLP techniques to extract features like named entities and keywords is common.

Topic classification involves categorizing text documents into predefined topics. Techniques used include Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), and neural networks. The process often involves dimensionality reduction steps like TF-IDF.

Product review rating prediction involves analyzing customer reviews to predict the rating that the review implies. Regression models such as linear regression, decision trees, and deep learning models like RNNs are used, sometimes treating the problem as a classification task.

Intent recognition identifies the user's intention behind a piece of text, which is crucial in natural language understanding and dialogue systems. Approaches vary from pattern matching to sophisticated models like RNNs, GRUs, and transformers.

Email categorization is the process of automatically sorting emails into folders or labels like 'Personal', 'Work', 'Promotions', etc. Techniques used include k-NN, decision trees, and neural networks, relying heavily on both content and metadata.

This problem is about classifying SMS messages as spam (unsolicited) or ham (legitimate). The approach often involves the use of a Naive Bayes classifier or other machine learning algorithms, with textual feature extraction being a key component of the process.