當今，個人化推薦服務大多基於使用者的興趣提取，但這些方法的結果通常是模棱兩可的。這讓了解個人興趣甚至使用者對興趣的感受更加困難。通過同時研究使用者的興趣以及情緒。可以建立更好的興趣提取的模型和演算法，並貢獻於個人化推薦和以及相關服務（如廣告推薦，社交網絡和交友網站）。在本文中，我們提出使用意見挖掘技術（情感分類）來模使用者的個人興趣上微博數據的新方法。我們的研究結果和使用者的真實興趣趣非常一致。從本質上講，我們分析不同的正面情緒和興趣的交互關係以及如何利用情緒來提取興趣。我們的實驗結果表明，使用者的情緒感受與他的興趣是相關聯的

Today, most personalized and recommendation services are built around user interest extraction models but the outputs of these algorithms are ambiguous in nature. This makes it inherently difficult to understand what users are personally interested in and more importantly what they are feeling towards these interests. By studying both users' interests and emotions, simultaneously, one can further investigate the motivation behind a user's interests/intentions. Such findings can be useful to build better interest extraction models and algorithms that leverage personalized and recommendation services (e.g. ads. recommendation, professional social networks and dating sites). In this paper, we propose a new approach that uses an opinion mining technique (emotion classification) to model user personal interests on microblog data. The interests identified by our method are very consistent with a user's real and personal interests. Essentially, we analyze the contribution degrees of different positive emotions in regards to how they assist in extracting a user's interests. Our experimental results indicates that a user's emotion-bearing information can provide empirical evidence to his/her true personal interests.

Introduction 1
Related Work 4
Overview 7
Methodology 11
4.1 Pre-processing 11
4.1.1 Hyperlinks - Factual Tweets 12
4.1.2 Repeated Tweets 13
4.2 Interest Candidates Extraction 14
4.2.1 POS Tagging 15
4.2.2 Keyword Extraction - RAKE 16
4.2.3 Rule-Based Extraction - Wisdom of the Crowd 18
4.2.4 Interest Candidate Pruning 21
4.2.5 Final Interest Candidates 22
4.3 Emotion Analysis 23
4.3.1 Emotion Classifier 24
4.3.2 Graph-Based Patterns Extraction 25
4.3.3 Ambiguous Tweets 27
4.3.4 Emotion Interest Tagging 28
4.4 Interest Identification 29
Experiments 32
5.1 Experimental Setup 32
5.2 Experiment 1: Users’ ratings 33
5.3 Experiment 2: Online Surveys 36
5.3.1 Hierarchical Interests Extraction 37
5.3.2 Evaluation 39
Conclusion 44

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