Bias Variance

در ادامه معرفی اجمالی emotion analysis در NLP و یک مقاله مروری قرار می گیرد. برای این زمینه می توانید پایان نامه و مقالات متعددی را مشاهده کنید. فرض کنید که یک سایت با تعداد کاربر بالا داشته باشید و محصولی تعداد ده هزار نظر برایش ثبت شده باشد. در این صورت نیاز به هوشی که بتواند به این نظرات نمره دهد کاملا حس می شود تا صاحب سایت به سرعت متوجه شود کاربران از محصول راضی بوده اند یا خیر.

A survey on deep learning for textual emotion analysis in social networks

Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states in texts. There has been rapid development of various Deep Learning (DL) methods that have proven successful in many domains such as audio, image, and natural language processing. This trend has drawn increasing numbers of researchers away from traditional machine learning to DL for their scientific research. In this paper, we provide an overview on TEA based on DL methods. After introducing a background for emotion analysis that includes defining emotion, emotion classification methods, and application domains of emotion analysis, we summarize DL technology,and the word/sentence representation learning method. We then categorize existing TEA methods based on text structures and linguistic types: text-oriented monolingual methods, text conversations-oriented monolingual methods, text-oriented cross-linguistic methods,and emoji-oriented cross-linguistic methods. We close by discussing emotion analysis challenges and future research trends. We hope that our survey will assist interested readers in understanding the relationship between TEA and DL methods while also improving TEA development.

#پردازش_زبان_طبیعی #emotion_analysis #مقاله_مروری #معرفی_مقاله #NLP

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A survey on deep learning for textual emotion analysis in social networks

Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states in texts. Various Deep Learning (DL) methods have developed rapidly, …

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167 viewsBias Variance, edited  Oct 19, 2021 at 16:39

در ادامه معرفی اجمالی emotion analysis در NLP و یک مقاله مروری قرار می گیرد. برای این زمینه می توانید پایان نامه و مقالات متعددی را مشاهده کنید. فرض کنید که یک سایت با تعداد کاربر بالا داشته باشید و محصولی تعداد ده هزار نظر برایش ثبت شده باشد. در این صورت نیاز به هوشی که بتواند به این نظرات نمره دهد کاملا حس می شود تا صاحب سایت به سرعت متوجه شود کاربران از محصول راضی بوده اند یا خیر.

A survey on deep learning for textual emotion analysis in social networks

Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states in texts. There has been rapid development of various Deep Learning (DL) methods that have proven successful in many domains such as audio, image, and natural language processing. This trend has drawn increasing numbers of researchers away from traditional machine learning to DL for their scientific research. In this paper, we provide an overview on TEA based on DL methods. After introducing a background for emotion analysis that includes defining emotion, emotion classification methods, and application domains of emotion analysis, we summarize DL technology,and the word/sentence representation learning method. We then categorize existing TEA methods based on text structures and linguistic types: text-oriented monolingual methods, text conversations-oriented monolingual methods, text-oriented cross-linguistic methods,and emoji-oriented cross-linguistic methods. We close by discussing emotion analysis challenges and future research trends. We hope that our survey will assist interested readers in understanding the relationship between TEA and DL methods while also improving TEA development.

#پردازش_زبان_طبیعی #emotion_analysis #مقاله_مروری #معرفی_مقاله #NLP

🌴 سایت | 🌺 کانال | 🌳 پشتیبانی

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