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I think you are definitely on a very sensible track. No one defines right or wrong in emotion field. It's not hard science. It's all theories. I have recently read a paper regarding emotions in Reinforcement Learning (RL). It has explained briefly emotion from 3 perspectives: psychology, neuroscience and computer science. In particular, your way of ...


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It could work using supervised learning , as long as you have the required dataset. However, a low error ratio using unsupervised learning of the human emotion spectrum would prove to be more difficult. Ex : How would you defined being in love to a neural network ? Joy +1 , Sadness -1 ? Now , How would you define being in love with , let’s say, someone ...


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For best results, I'd recommend Google Cloud Machine Learning. It has [Natural Language Processing API] (https://cloud.google.com/natural-language/docs/basics) with Sentiment, Entity, and Entity-Sentiment analysis. You can implement in C++, PHP, Python, or other languages. This does require running a virtual machine instance on Google Cloud. TensorFlow can ...


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It's just as in common use. Sentiment is a superset of emotion, further including feelings, intuition, and rational conclusions drawn from past experience. Analysis is also a superset of recognition, which also includes taking emotions like contentment, happiness, humor affected, anger, rage, and passion and determining what sequences of them mean in the ...


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Hinge loss is difficult to work with when the derivative is needed because the derivative will be a piece-wise function. max has one non-differentiable point in its solution, and thus the derivative has the same. This was a very prominent issue with non-separable cases of SVM (and a good reason to use ridge regression). Here's a slide (Original source from ...


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I interpret the question so, that the OP wants to know in which direction a literature search has to go. Because the topic he is asking for is very well researched by academics before. At first it is important to know, that the term CRM (Customer Relationship Management) is used in the business literature for describing marketing strategies and also for ...


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It depends very much on the structure of the data. I would think about feature extraction first, which could be certain words occurring in the bio, and a class of user name ('real' name, numerical id, etc). Once you have a set of features for each data item, turn them into a list of feature vectors. Then run them through a number of machine learning ...


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I don't want to pour cold water over your approach, but I am very sceptical and (having worked in sentiment analysis myself) think it is way too simplistic. Various communicative intents are encoded in language, and there is a wide range of linguistic features that are employed for that purpose. Choice of words is only one of them; it is the most obvious ...


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