Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Compared to the categorical approach that represents affective states as several discrete classes e.g., positive and negative, the dimensional approach represents affective states as continuous numerical values in multiple dimensions, such as the valence-arousal VA space, thus allowing for more fine-grained sentiment analysis. In building dimensional sentiment applications, affective lexicons with VA ratings are useful resources but are still very rare. Several semi-supervised methods such as the kernel method, linear regression, and the pagerank algorithm have been investigated to automatically determine the VA ratings of affective words from a set of semantically similar seed words. These methods suffer from two major limitations. First, they apply an equal ight to all seeds similar to an unseen word in predicting its VA ratings. Second, even similar seeds may have quite different ratings or an inverse polarity of valence/arousal to the unseen word, thus reducing prediction performance. To overcome these limitations, this study proposes a community-based ighted graph model that can select seeds which are both similar to and have similar ratings or the same polarity with each unseen word to form a community subgraph so that its VA ratings can be estimated from such high-quality seeds using a ighted propagation scheme. That is, seeds more similar to unseen words contribute more to the estimation process. Experimental results show that the proposed method yields better prediction performance for both English and Chinese datasets.
['Jin Wang', 'Liang-Chih Yu', 'K. Robert Lai', 'Xuejie Zhang']
I am writing a research paper on a idea Community-Based Weighted Graph Model for Valence-Arousal Prediction of Affective Words
[{"from": "human", "value": "I am writing a research paper on a idea Community-Based Weighted Graph Model for Valence-Arousal Prediction of Affective Words"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Compared to the categorical approach that represents affective states as several discrete classes e.g., positive and negative, the dimensional approach represents affective states as continuous numerical values in multiple dimensions, such as the valence-arousal VA space, thus allowing for more fine-grained sentiment analysis. In building dimensional sentiment applications, affective lexicons with VA ratings are useful resources but are still very rare. Several semi-supervised methods such as the kernel method, linear regression, and the pagerank algorithm have been investigated to automatically determine the VA ratings of affective words from a set of semantically similar seed words. These methods suffer from two major limitations. First, they apply an equal ight to all seeds similar to an unseen word in predicting its VA ratings. Second, even similar seeds may have quite different ratings or an inverse polarity of valence/arousal to the unseen word, thus reducing prediction performance. To overcome these limitations, this study proposes a community-based ighted graph model that can select seeds which are both similar to and have similar ratings or the same polarity with each unseen word to form a community subgraph so that its VA ratings can be estimated from such high-quality seeds using a ighted propagation scheme. That is, seeds more similar to unseen words contribute more to the estimation process. Experimental results show that the proposed method yields better prediction performance for both English and Chinese datasets."}]