Return to search

Commercial intention detection on Twitter. / 推特上的商業意圖檢測 / Tuite shang de shang ye yi tu jian ce

Zhu, Yi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 136-148). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Motivations of Detecting Commercial Intention --- p.4 / Chapter 1.3 --- Problem Definition for Commercial Intention Detection --- p.6 / Chapter 1.4 --- Contributions --- p.8 / Chapter 1.5 --- Thesis Organization --- p.9 / Chapter 2 --- Literature Review --- p.12 / Chapter 2.1 --- Twitter and Tweets Analysis --- p.13 / Chapter 2.2 --- Intention Detection --- p.17 / Chapter 2.2.1 --- User Intention Mining --- p.17 / Chapter 2.2.2 --- Commercial Intention Mining --- p.18 / Chapter 2.3 --- Similar Task: Opinion Mining --- p.18 / Chapter 2.4 --- NLP Techniques for Commercial Intention Detection --- p.20 / Chapter 2.4.1 --- Words Semantic Similarity --- p.21 / Chapter 2.4.2 --- Short Text Similarity --- p.25 / Chapter 2.5 --- Hierarchical Classification --- p.26 / Chapter 2.5.1 --- Hierarchical Classifiers Overview --- p.26 / Chapter 2.5.2 --- Construction of Hierarchy --- p.27 / Chapter 2.5.3 --- Taxonomy of Hierarchical Classification --- p.28 / Chapter 3 --- System Overview --- p.31 / Chapter 3.1 --- Feasibility of Commercial Intention Detection --- p.31 / Chapter 3.2 --- System Design and Architecture --- p.33 / Chapter 3.3 --- Components of READ-MIND --- p.35 / Chapter 3.3.1 --- Preprocessing --- p.35 / Chapter 3.3.2 --- Centroid Word Locator --- p.37 / Chapter 3.3.3 --- Commercial Intention Detector --- p.38 / Chapter 3.3.4 --- Tweet Classifier --- p.40 / Chapter 3.3.5 --- Advertisement Mapping --- p.41 / Chapter 3.4 --- System Work Flow --- p.42 / Chapter 3.4.1 --- System Dataflow and Controlflow --- p.42 / Chapter 3.4.2 --- User Interface --- p.42 / Chapter 3.5 --- System Speed Up --- p.43 / Chapter 3.6 --- Summary --- p.45 / Chapter 4 --- Natural Language Processing on Tweets --- p.46 / Chapter 4.1 --- NLP Techniques in READ-MIND --- p.46 / Chapter 4.2 --- Centroid Word Locator --- p.47 / Chapter 4.2.1 --- Centroid Word --- p.47 / Chapter 4.2.2 --- Locating Centroid Word --- p.48 / Chapter 4.2.3 --- Centroid Word Pair --- p.50 / Chapter 4.2.4 --- Locating Centroid Word Pair --- p.54 / Chapter 4.3 --- Semantic Relatedness Between Tweets --- p.59 / Chapter 4.3.1 --- Relatedness with a Words Set --- p.60 / Chapter 4.3.2 --- Relatedness between Tweets --- p.62 / Chapter 4.3.3 --- Words Similarity --- p.63 / Chapter 4.4 --- Summary --- p.65 / Chapter 5 --- Tweets Classification --- p.66 / Chapter 5.1 --- Two Stages of Tweets Classification --- p.66 / Chapter 5.2 --- Commercial Intention Detector --- p.68 / Chapter 5.2.1 --- Intuitive Method --- p.68 / Chapter 5.2.2 --- Binary Classification --- p.70 / Chapter 5.3 --- Tweet Categorization --- p.72 / Chapter 5.3.1 --- Build Hierarchical Classifier --- p.73 / Chapter 5.3.2 --- Hierarchical Classification --- p.81 / Chapter 5.4 --- Summary --- p.83 / Chapter 6 --- Empirical Study --- p.84 / Chapter 6.1 --- Objective of Empirical Study --- p.84 / Chapter 6.2 --- Experiment Setup and Evaluation Methodology --- p.85 / Chapter 6.2.1 --- Simulation Environment --- p.85 / Chapter 6.2.2 --- Tweets Data Set --- p.86 / Chapter 6.2.3 --- Labeling Process --- p.87 / Chapter 6.2.4 --- Evaluation Methodology --- p.88 / Chapter 6.3 --- Compare Algorithms in Components --- p.90 / Chapter 6.3.1 --- Centroid Word VS. Centroid Word Pair --- p.91 / Chapter 6.3.2 --- Semantic Similarity Comparison --- p.92 / Chapter 6.3.3 --- Methods in Commercial Intention Detector --- p.93 / Chapter 6.3.4 --- Structure of Hierarchy --- p.94 / Chapter 6.3.5 --- Training Source of Tweets Classifier --- p.95 / Chapter 6.3.6 --- Summary --- p.96 / Chapter 6.4 --- Parameter Settings Comparison --- p.97 / Chapter 6.4.1 --- Impact of Varying Parameters --- p.97 / Chapter 6.4.2 --- Discussion on Parameter Setting --- p.98 / Chapter 6.5 --- Comparison of READ-MIND and Baseline Method --- p.100 / Chapter 6.6 --- Time Cost Analysis --- p.101 / Chapter 6.6.1 --- Time Cost to Process Tweets --- p.101 / Chapter 6.6.2 --- Comparison with Baseline --- p.102 / Chapter 6.6.3 --- Analysis on Real-Time Property --- p.103 / Chapter 6.7 --- TCI Categories Comparison --- p.106 / Chapter 6.7.1 --- Results for Different TCIs --- p.106 / Chapter 6.7.2 --- Comparison of Different TCIs --- p.107 / Chapter 6.8 --- Summary --- p.108 / Chapter 7 --- Conclusion --- p.109 / Chapter 7.1 --- Conclusion --- p.109 / Chapter 7.2 --- Future Work --- p.111 / Chapter A --- List of Abbreviations --- p.112 / Chapter B --- List of Symbols --- p.114 / Chapter C --- Proof --- p.117 / Chapter D --- System Work Flow --- p.120 / Chapter E --- Algorithms --- p.123 / Chapter F --- Detailed Experimental Results --- p.129 / Bibliography --- p.136

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_327393
Date January 2011
ContributorsZhu, Yi., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering.
Source SetsThe Chinese University of Hong Kong
LanguageEnglish, Chinese
Detected LanguageEnglish
TypeText, bibliography
Formatprint, xv, 148 p. : ill. ; 30 cm.
RightsUse of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Page generated in 0.0022 seconds