本研究提出了一種基於面向的情緒預測複合模型，它結合了張量分解以及意見效用邏輯模型。首先，利用情緒詞典作為種子，通過雙向傳播方法以迭代的方式擴展面向詞彙以及情緒詞彙。因此，使用者評論可以表示為使用者-項目-面向三維空間內的特徵，於是我們可以藉此來建立起我們的模型。本篇論文提出了複合模型的各種不同組合，並對TripAdvisor中文旅遊評論網在台灣省宜蘭縣的旅遊景點評論進行分析、建構模型以及預測使用者總體評價。實驗結果顯示，複合模型可以獲得比張量分解或意見效用邏輯模型更好的預測效果。另外，在處理新使用者的冷啟動問題時，複合模型也優於張量分解或意見效用邏輯模型。

This paper proposes a hybrid model of aspect-oriented sentiment prediction which integrates tensor factorization (TF) and sentiment utility logistic model (SULM). First, using sentiment dictionary words as seeds, the aspect or opinion words can be extended iteratively through double propagation. Accordingly, the users’ reviews could be represented as the features in user-item-aspect space, in which prediction model could be built. Various combinations of the hybrid model were proposed and evaluated on the Chinese reviews on places of interest at Taiwan Yilan from TripAdvisor. Experimental results show that the hybrid model can achieve better prediction performance than TF or SULM. The hybrid model also outperforms either TF or SULM while handling new user’s cold-start problem.

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