Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In recent years many people have begun to express their thoughts and opinions on Twitter. Naturally, Twitter has become an effective source to investigate people’s emotions for numerous applications. Classifying only positive and negative tets has been exploited in depth, whereas analyzing finer emotions is still a difficult task. More elaborate emotion lexicons should be developed to deal with this problem, but existing lexicon sets are mostly in English. Moreover, building such lexicons is known to be extremely laborintensive or resource-intensive. Finer-grained features need to be taken into account when determining finer-emotions, but many existing works still utilize coarse features that have been widely used in analyzing only the polarity of emotion. ,  present a method to automatically build fine-grained emotion lexicon sets and suggest features that improve the performance of machine learning based emotion classification in Korean Twitter texts.
['Hyo Jin Do', 'Ho-Jin Choi']
I am writing a research paper on a idea Korean Twitter Emotion Classification Using Automatically Built Emotion Lexicons and Fine-Grained Features
[{"from": "human", "value": "I am writing a research paper on a idea Korean Twitter Emotion Classification Using Automatically Built Emotion Lexicons and Fine-Grained Features"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In recent years many people have begun to express their thoughts and opinions on Twitter. Naturally, Twitter has become an effective source to investigate people\u2019s emotions for numerous applications. Classifying only positive and negative tets has been exploited in depth, whereas analyzing finer emotions is still a difficult task. More elaborate emotion lexicons should be developed to deal with this problem, but existing lexicon sets are mostly in English. Moreover, building such lexicons is known to be extremely laborintensive or resource-intensive. Finer-grained features need to be taken into account when determining finer-emotions, but many existing works still utilize coarse features that have been widely used in analyzing only the polarity of emotion. ,  present a method to automatically build fine-grained emotion lexicon sets and suggest features that improve the performance of machine learning based emotion classification in Korean Twitter texts."}]