Show HN: NLP algorithms for real-world sentiment analysis

Understanding customer feelings is very important for growing your business. Sentiment analysis helps to get insights and enhance the customer experience. There are NLP algorithms that helps you to classify people's emotions. 1/ Naïve Bayes This technique is based on Bayes Theorem, a conditional probability model. Basically, a Naïve Bayes classifier assumes the features are independent. 2/ SVM It classifies text by finding a line or plane that separates data points into different groups. These points are called support vectors. This algorithm is good at dealing with noisy data. 3/ Deep learning It is used very successful in classifying text. Common algorithms are Recurrent neural networks (RNNs), Word2Vec, GloVe, and Convolutional Neural Networks (CNN). 4/ Transformers This architecture is a breakthrough in NLP. Helps computers understand text and its context. Very good at doing natural language processing than other kinds of neural networks. Start using them for text classification with @huggingface pre-trained models. Do you have real-world experience using those algorithms for sentiment analysis? Please, share your thoughts or provide a feedback? Origina article => https://pxla.xyz/ZkL6Prp