The emotions one experiences daily can motivate them to act and influence the significant and minor decisions they make in their lives. Therefore, they greatly influence how people socialize and form connections.
Communication helps us to express a vast range of delicate and complicated emotions with only a few words. With recent advancements in NLP, several datasets for language-based emotion categorization have been made accessible. The majority of them focus on specific genres (news headlines, movie subtitles, and even fairy tales) and the six primary emotions (anger, surprise, disgust, joy, fear, and sadness). There is, therefore, a need for a larger-scale dataset covering a greater range of emotions to allow for a broader range of possible future applications.
A recent Google study introduces GoEmotions: a human-annotated dataset of fine-grained emotions with 58k Reddit comments taken from major English-language subreddits and 27 emotion categories identified. It has 12 positive, 11 negatives, 4 ambiguous emotion categories, and 1 “neutral” emotion category, making it broadly useful for conversation interpretation tasks that demand delicate discrimination between emotion displays. They also demonstrate a full tutorial that shows how to use GoEmotions to train a neural model architecture and apply it to recommending emojis based on conversational text.
Quick Read | Paper| Google Blog
I recently found out about the "emotions wheel". Here's an example: image and associated article (this one is called a "feelings wheel").
It's basically a classification system for emotions, albeit one that is rather rudimentary and unrefined imo. And some of the items are not emotional states but attitudes.
Apparently, you're supposed to read the wheel from the inside out. You start with the innermost circle. Each of those items is subdivided into more specific items, and each of those items is subdivided further, and so on, as hierarchies go. You start with a more general emotion and narrow down to a more specific one.
Curious what you think about this feelings wheel concept? Socionically, it seems like a FeTi-valuer thing.
I think the concept is really cool, lol, but I already (after a quick look) take issue with several of the classifications. For example, in one section, they have:
happy
powerful
courageous
creative
I think creative does not belong under powerful. It's maybe better under optimistic or interested... But, hm, actually, I don't think creative or interested are emotional states. They're mental states/states of mind, but not typically emotional ones. To me, interest is a matter of attention, which is more about mental engagement than emotional engagement.
Also, I don't think powerful is always a happy emotional state; it can also result from anger, for example. So I wouldn't bucket powerful under happy any more than I'd bucket it under angry or even sad.
IMO, there are a lot of misclassifications like this throughout the wheel. But, to be fair, I'm not sure how I'd classify all of these items either.
Also, fundamentally, it needs a lot more options, and accordingly, more categories and more layers. It's very limited and not comprehensive.
Also, I think different people sometimes use the same word to describe different emotions. So that might be enough to render the whole thing pointless as a resource intended for widespread use.
I couldn't find a labeled text dataset to do emotions classification (Joy, Sadness, Anger ...) In French language. So, I thought about translating an English Dataset (labelled) to French and train a model on it.
Is it a good idea, or can the translation cause some kind of bias? Any suggestions or ideas?
Hello
I am a student and I am working on emotion analysis with deep learning. My supervisor asked me to extract semantic features from the text ( convert raw data into useful semantic features ) before using deep learning. But I am confused when I read research about text classification with deep learning. DL does not need feature extraction only using different types of word embedding to convert data, I need to clarify this. Is it possible to extract features before applying Deep Learning.? Is there any research that can help me with that? Any suggestions tools or techniques to extract semantic features from the text.
Is there an API that performs emotion classification from speech files?
Hi everyone! Here's the survey: https://forms.gle/u3CMP5B3pP4rT9Yg6. It should take 5~10 minutes. Really appreciate anyone who participates as it helps out a lot! Have a nice day :).
Hope the title got you hooked :) I was speaking with a friend about synesthesia and how some people see emotions as colors. It made me remember the “emotion wheel” diagram: http://blog.thejuntoinstitute.com/the-junto-emotion-wheel-why-and-how-we-use-it
Our paper describing emotion detection in tweets, using a simple multi-class perceptron model and n-grams as feature set:
https://aclanthology.coli.uni-saarland.de/papers/W18-6235/w18-6235
My agency gave me a placement at a special education highschool with students that mostly have classification of “emotionally disturbed” and are known to be violent with adults and teachers. As a back story, I put in my 30 days notice to quit with this agency so I need to survive at this school for 3 weeks. They just placed me there right as I put in my notice to quit.
It's behaviors such as stabbing teachers, punching in the face, throwing tables etc.
Anyway, I’m seeking advice from those of you that have worked with students in this setting. I am a little nervous because I do not have experience with it and am small sized girl.
The emotions one experiences daily can motivate them to act and influence the significant and minor decisions they make in their lives. Therefore, they greatly influence how people socialize and form connections.
Communication helps us to express a vast range of delicate and complicated emotions with only a few words. With recent advancements in NLP, several datasets for language-based emotion categorization have been made accessible. The majority of them focus on specific genres (news headlines, movie subtitles, and even fairy tales) and the six primary emotions (anger, surprise, disgust, joy, fear, and sadness). There is, therefore, a need for a larger-scale dataset covering a greater range of emotions to allow for a broader range of possible future applications.
A recent Google study introduces GoEmotions: a human-annotated dataset of fine-grained emotions with 58k Reddit comments taken from major English-language subreddits and 27 emotion categories identified. It has 12 positive, 11 negatives, 4 ambiguous emotion categories, and 1 “neutral” emotion category, making it broadly useful for conversation interpretation tasks that demand delicate discrimination between emotion displays. They also demonstrate a full tutorial that shows how to use GoEmotions to train a neural model architecture and apply it to recommending emojis based on conversational text.
