Dear Aouamria Selma ,
Over the last decade Deep learning-based models surpasses classical machine learning models in a variety of text classification tasks. The primary challenge with text classification is determining the most appropriate deep learning classifier.
Article An effective ensemble deep learning framework for text classification
Regards,
Shafagat
Deep Neural Network (DNN) models are widely used for image classification. While they offer high performance in terms of accuracy, researchers are concerned about if these models inappropriately make inferences using features irrelevant to the target object in a given image
Long short-term memory (LSTM) is a deep recurrent neural network architecture used for the classification Like time-series data.
Sentiment Analysis / Text Classification Using CNN (Convolutional Neural Network) There are lots of applications of text classification. For example, hate speech detection, intent classification, and organizing news articles.
https://www.nature.com/articles/s41598-021-93656-0
https://medium.com/analytics-vidhya/image-classification-a-comparison-of-dnn-cnn-and-transfer-learning-approach-704535beca25
GOOD LUCK
Yes, you can use any of your proposed approaches,. however, model or Classifier selection may be based on quality and amount of data at hand, see e.g., https://nlp.stanford.edu/IR-book/html/htmledition/choosing-what-kind-of-classifier-to-use-1.html
It depends on your actual needs, such as your focus on performance or efficiency. Intuitively, if you need to deploy in a production environment, a simple and efficient model is worth advocating. The voting of ensemble learning is more applicable to multiple foundation models independent of each other. The degree of independence is hard to define, so you can see if the results of the vote can significantly improve performance. Again, DNN doesn't mean much, because just pure full connection can be included by CNN and LSTM. In addition, CNN seems not suitable for sequential tasks, so TCN can be considered to replace CNN.
Yes you can. Deep learning algorithms predicts better if there is a large dataset, so it is preferred.
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