Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  develop a deep learning system for message-level Twitter sentiment classification. Among the 45 submitted systems including the SemEval 2013 participants, our system (Coooolll) is ranked 2nd on the Twitter2014 test set of SemEval 2014 Task 9. Coooolll is built in a supervised learning framework by concatenating the sentiment-specific word embedding (SSWE) features with the state-of-the-art hand-crafted features.  develop a neural network with hybrid loss function 1 to learn SSWE, which encodes the sentiment information of tets in the continuous representation of words. To obtain large-scale training corpora,  train SSWE from 10M tets collected by positive and negative emoticons, without any manual annotation. Our system can be easily re-implemented with the publicly available sentiment-specific word embedding.
['Duyu Tang', 'Furu Wei', 'Bing Qin', 'Ting Liu', 'Ming Zhou']
I am writing a research paper on a idea Coooolll: A Deep Learning System for Twitter Sentiment Classification
[{"from": "human", "value": "I am writing a research paper on a idea Coooolll: A Deep Learning System for Twitter Sentiment Classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  develop a deep learning system for message-level Twitter sentiment classification. Among the 45 submitted systems including the SemEval 2013 participants, our system (Coooolll) is ranked 2nd on the Twitter2014 test set of SemEval 2014 Task 9. Coooolll is built in a supervised learning framework by concatenating the sentiment-specific word embedding (SSWE) features with the state-of-the-art hand-crafted features.  develop a neural network with hybrid loss function 1 to learn SSWE, which encodes the sentiment information of tets in the continuous representation of words. To obtain large-scale training corpora,  train SSWE from 10M tets collected by positive and negative emoticons, without any manual annotation. Our system can be easily re-implemented with the publicly available sentiment-specific word embedding."}]