Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Word representation is crucial to lexical features used in Twitter sentiment analysis models. Recent work has demonstrated that dense, low-dimensional and real-valued word embedding gives competitive performance for Twitter sentiment classification.  follow this line of work, and propose a topic-enhanced word embedding for the task, which is generally neglected in previous work. Firstly,  exploit a recursive autoencoder framework to learn topic-enhanced word embedding, where topic information is generated through topic modeling based on an effective implementation of Latent Dirichlet Allocation (LDA). Then  use a uniform framework by adopting Support Vector Machine (SVM) classifier, to compare existing word representation methods with our method. Experimental results on the dataset show that topic-enhanced word embedding is very effective for Twitter sentiment classification.
['Yafeng Ren', 'Ruimin Wang', 'Donghong Ji']
I am writing a research paper on a idea A topic-enhanced word embedding for Twitter sentiment classification
[{"from": "human", "value": "I am writing a research paper on a idea A topic-enhanced word embedding 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: Word representation is crucial to lexical features used in Twitter sentiment analysis models. Recent work has demonstrated that dense, low-dimensional and real-valued word embedding gives competitive performance for Twitter sentiment classification.  follow this line of work, and propose a topic-enhanced word embedding for the task, which is generally neglected in previous work. Firstly,  exploit a recursive autoencoder framework to learn topic-enhanced word embedding, where topic information is generated through topic modeling based on an effective implementation of Latent Dirichlet Allocation (LDA). Then  use a uniform framework by adopting Support Vector Machine (SVM) classifier, to compare existing word representation methods with our method. Experimental results on the dataset show that topic-enhanced word embedding is very effective for Twitter sentiment classification."}]