Prevalent Machine Learning Strategies for Mood Forecasting
Machine learning strategies are extensively employed in mood forecasting, particularly within the realm of text analysis. Here are some prevalent machine learning strategies used for mood forecasting:
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Traditional Machine Learning Classifiers: These classifiers have been harnessed for detecting and predicting moods from text due to their satisfactory performance with smaller training data sets, reduced training time, and lower processing power needs. Examples of these include Decision Support Vector Machine and Multinomial Naive Bayes.
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Linear and Nonlinear Techniques: Linear techniques such as lasso and neural nets, along with nonlinear techniques, have been utilized for mood forecasting using data derived from smartphones and wearable devices.
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Passive Digital Phenotypes: Machine learning models constructed using passive digital phenotypes based on circadian rhythms have been successful in predicting the mood states of patients suffering from mood disorders. These models have demonstrated high precision in forecasting future mood states.
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Text Analysis via Machine Learning: A variety of machine learning methods like kNN, Support Vector Machine (SVM), Artificial Neural Networks (ANNs), Naïve Bayes (NB), and deep learning through neural networks have been implemented for emotion detection via text analysis.
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Lexicon-Based Methodology: The detection of emotions through a lexicon-based methodology combined with traditional machine learning methods such as Naïve Bayes, SVM, and deep learning neural networks has proven effective in identifying emotions within text.
These strategies harness the capabilities of machine learning to scrutinize text data and accurately predict moods. They hold a pivotal role in domains like psychology, marketing, network analysis among others.
