本篇論文中，一個基於調節過程模型的整合情感調節系統(IERS)被提出應用於提升幸福感。除了從使用者在社群網站中的內容解析出有價值的資訊外，IERS分析了使用者的情感變化將其對應到調節過程模型，並且針對這些情感變化適當給使用者回饋。我們從調節語料庫中選擇正面且激勵的回饋文字。本篇提出的IERS除了工作在單字層級的運算外，對於從Facebook塗鴉牆上收集的語料，採用支援向量基進行情緒主題的分類；同時回饋策略的選擇是由點對點相互資訊的特徵擷取決定。對於本篇七個情緒類別的辨認精準度可以達到超過50%。在20個受試者參與一周的實驗中，藉由觀察實驗的前測與後測結果，可以發現本篇提出的系統確實能夠提升幸福感。

In this thesis, an integrated emotion regulation system (IERS) is proposed based on the regulation process model for happiness improvement. Including extracting the valuable information from user’s contents on social network, the IERS analyzes users’ emotion variation reflecting to the regulation process model and aim to appropriately feedback to users. The feedback sentences are chosen from regulation corpus which is positive and motivated. The proposed IERS works at the word level and the emotional topics is classified by Support vector machine (SVM) through the corpus collected from Facebook wall, whereas feedback strategy is chosen through Point-Wise Mutual Information (PMI) features extraction. The accuracy result of seven-type emotion recognition can achieve higher than 50%. The pre- and post-experiment results are evaluated by 20 participants in one week of observation, of which the result implies the proposed system can practically improve the happiness.

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