隱含狄利克雷分佈是主題模型中相當熱門的研究題目，其原因在於隱含狄利克雷可以經由修改內部的參數及相依性，即可適用於不同的題目上，例如：情緒分析、輿情分析甚至是圖像分析等等，也由於其高度彈性，使得相關研究都著重於提出新的相依性已取得更好的結果。
然而，現實事件的資料分佈、特性相當多元，單一相依性的模型可能忽略每筆資料的特性，此外，不同的資料來源間的差異，可能導致分析結果變差。因此，我們提出多相依性隱含狄利克雷分佈，主要是利用多相依性以符合資料特性，不論其資料原始差異為何，皆能從相依性的集合內取出最適合之的結果，我們同時加入過去曾被提出的模型之相依性，以探討多相依性隱含狄利克雷是否能獲得更優秀的成果，我們也預期，即使有一特殊資料來源，其資料全部屬於特定一種相依性且完美符合某一特定模型，隱含狄利克雷仍舊能獲得與之相同的結果。我們的實驗結果證明，隱含多相依性狄利克雷的確能夠增進不同模型的效能。

Latent Dirichlet Allocation (LDA) is an attractive topic model research because LDA is so flexible for solving different problems. Because of its different core dependencies, it can be applied to many topics, such as emotion detection, information systems or image clustering. In recent works, researchers have focused on novel dependency to obtain perfect fitting to datasets. However, real world data is too diverse and abundant to be fitted with one single dependency. A single dependency model can only concentrate on the overall characteristic of datasets and thus ignores small details in the data. As a result, we propose Multi-dependent Latent Dirichlet Allocation (MD-LDA). MD-LDA can be applied various dependencies into the model. For each piece of data, MD-LDA can pick up the most optimal fitting dependency from the dependency set and therefore obtain the best dependencies for the dataset. We also apply some previous works into MD-LDA as a basis for comparison. In our experiments, MD-LDA exhibits the best performance in various cases and is an improvement compared to the other models under consideration.

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