事實證明，性格偵測在許多領域是有用的。 五大性格特質模型和邁爾斯-布里格斯性格分類指標 是常見的性格選擇，但它們僅適用於某些行業，例如教育、商業。九型人格特徵可以提供更深入以及更易懂的個人資訊，並且更適合用在心理學和精神病學等領域。 本研究提出了基於九型人格特徵的人格分類，目的是提供自動性格偵測並了解每種性格類型用戶的在線互動的行為。 就結果來說，和詞袋功能相比，表情符號和語言特徵（例如語言查詢和單詞計數（LIWC）和詞性標籤（POS標籤））在跨源性格偵測中顯得更穩定和更泛化並且邏輯回歸證明在所有特徵和所有性格類型上都能正常工作。

Personality detection has been proved useful in many sectors. While Five Factor Model and Myers-Briggs Type Indication are common personality choice, they are only suitable for certain industries such as Education, Business. Enneagram personality could provide deeper and more understanding information of an individual and is more suitable for the fields like Psychology and Psychiatry. This work proposed the personality classification based on Enneagram personality with the aim of providing automatic personality detection framework and understanding the online interaction behaviors of the users in each personality type. As a result, compared to Bag-of-word features, emoticon and linguistic features such as Linguistic Inquiry and Word Counts (LIWC) and Part-of-speech tagging (POS Tag) appeared to be more robust and generalizable in cross-source personality detection while Logistic Regression proved to work generally well across all features and all personality types.

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