Current sentiment analysis on Twitter is hampered by two factors namely, not all accounts are genuine and not all users have the same level of influence. Including non credible and irrelevant Tweets in sentiment analysis dilutes the effectiveness of any sentiment produced. Similarly, counting a Tweet with a potential audience of 10 users as having the same impact as a Tweet that could reach 1 million users is not accurately reflecting its importance. In order to mitigate against these inherent problems a novel method was devised to account for credibility and to measure influence. The current definition of credibility on Twitter was redefined and expanded to incorporate the subtle nuances that exist beyond the simple variance between human or bot account. Once basic sentiment was produced it was filtered by removing non credible Tweets and the remaining sentiment was augmented by weighting it based upon both the user’s and the Tweet’s influence scores. Measuring one person’s opinion is costly and lacking in power, however, machine learning techniques allow us to capture and analyse millions of opinions. Combining a Tweet’s sentiment with the user’s influence score and their credibility rating greatly increases the understanding and usefulness of that sentiment. In order to gauge and measure the impact of this research and highlight its generalisability, this thesis examined 2 distinct real world datasets, the UK General Election 2015 and the Rugby World Cup 2015, which also served to validate the approach used. A better more accurate understanding of sentiment on Twitter has the potential for broad impact from providing targeted advertising that is in tune with people’s needs and desires to providing governments with a better understanding of the will and desire of the people.

Yang, Zhaojun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 92-99). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- SDS Architecture --- p.1 / Chapter 1.2 --- Dialog Model --- p.3 / Chapter 1.3 --- SDS Evaluation --- p.4 / Chapter 1.4 --- Thesis Outline --- p.7 / Chapter 2 --- Previous Work --- p.9 / Chapter 2.1 --- Approaches to Dialog Modeling --- p.9 / Chapter 2.1.1 --- Handcrafted Dialog Modeling --- p.9 / Chapter 2.1.2 --- Statistical Dialog Modeling --- p.12 / Chapter 2.2 --- Evaluation Metrics --- p.16 / Chapter 2.2.1 --- Subjective User Judgments --- p.17 / Chapter 2.2.2 --- Interaction Metrics --- p.18 / Chapter 2.3 --- The PARADISE Framework --- p.19 / Chapter 2.4 --- Chapter Summary --- p.22 / Chapter 3 --- Implementation of a Dialog System based on POMDP --- p.23 / Chapter 3.1 --- Partially Observable Markov Decision Processes (POMDPs) --- p.24 / Chapter 3.1.1 --- Formal Definition --- p.24 / Chapter 3.1.2 --- Value Iteration --- p.26 / Chapter 3.1.3 --- Point-based Value Iteration --- p.27 / Chapter 3.1.4 --- A Toy Example of POMDP: The NaiveBusInfo System --- p.27 / Chapter 3.2 --- The SDS-POMDP Model --- p.31 / Chapter 3.3 --- Composite Summary Point-based Value Iteration (CSPBVI) --- p.33 / Chapter 3.4 --- Application of SDS-POMDP Model: The Buslnfo System --- p.35 / Chapter 3.4.1 --- System Description --- p.35 / Chapter 3.4.2 --- Demonstration Description --- p.39 / Chapter 3.5 --- Chapter Summary --- p.42 / Chapter 4 --- Collecting User Judgments on Spoken Dialogs with Crowdsourcing --- p.46 / Chapter 4.1 --- Dialog Corpus and Automatic Dialog Classification --- p.47 / Chapter 4.2 --- User Judgments Collection with Crowdsourcing --- p.50 / Chapter 4.2.1 --- HITs on Dialog Evaluation --- p.51 / Chapter 4.2.2 --- HITs on Inter-rater Agreement --- p.53 / Chapter 4.2.3 --- Approval of Ratings --- p.54 / Chapter 4.3 --- Collected Results and Analysis --- p.55 / Chapter 4.3.1 --- Approval Rates and Comments from Mturk Workers --- p.55 / Chapter 4.3.2 --- Consistency between Automatic Dialog Classification and Manual Ratings --- p.57 / Chapter 4.3.3 --- Inter-rater Agreement Among Workers --- p.60 / Chapter 4.4 --- Comparing Experts to Non-experts --- p.64 / Chapter 4.4.1 --- Inter-rater Agreement on the Let's Go! System --- p.65 / Chapter 4.4.2 --- Consistency Between Expert and Non-expert Annotations on SDC Systems --- p.66 / Chapter 4.5 --- Chapter Summary --- p.68 / Chapter 5 --- Collaborative Filtering for Performance Prediction --- p.70 / Chapter 5.1 --- Item-Based Collaborative Filtering --- p.71 / Chapter 5.2 --- CF Model for User Satisfaction Prediction --- p.72 / Chapter 5.2.1 --- ICFM for User Satisfaction Prediction --- p.72 / Chapter 5.2.2 --- Extended ICFM for User Satisfaction Prediction --- p.73 / Chapter 5.3 --- Extraction of Interaction Features --- p.74 / Chapter 5.4 --- Experimental Results and Analysis --- p.76 / Chapter 5.4.1 --- Prediction of User Satisfaction --- p.76 / Chapter 5.4.2 --- Analysis of Prediction Results --- p.79 / Chapter 5.5 --- Verifying the Generalibility of CF Model --- p.81 / Chapter 5.6 --- Evaluation of The Buslnfo System --- p.86 / Chapter 5.7 --- Chapter Summary --- p.87 / Chapter 6 --- Conclusions and Future Work --- p.89 / Chapter 6.1 --- Thesis Summary --- p.89 / Chapter 6.2 --- Future Work --- p.90 / Bibliography --- p.92

No description available.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves [160]-165). Also available on the Internet.

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2004. / Includes bibliographical references (p. 37-38).

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves [160]-165). Also available on the Internet.

Thesis (Ed. D.)--University of Kentucky, 2002. / Title from document title page. Document formatted into pages; contains xix, 175 p. : ill. Includes abstract. Includes bibliographical references (p. 160-173).

published_or_final_version / Education / Doctoral / Doctor of Philosophy

Earth Mover's Distance (EMD), as a similarity measure, has received a lot of attention in the fields of multimedia and probabilistic databases, computer vision, image retrieval, machine learning, etc. EMD on multidimensional histograms provides better distinguishability between the objects approximated by the histograms (e.g., images), compared to classic measures like Euclidean distance. Despite its usefulness, EMD has a high computational cost; therefore, a number of effective filtering methods have been proposed, to reduce the pairs of histograms for which the exact EMD has to be computed, during similarity search. Still, EMD calculations in the refinement step remain the bottleneck of the whole similarity search process. In this thesis, we focus on optimizing the refinement phase of EMD-based similarity search by (i) adapting an efficient min-cost flow algorithm (SIA) for the EMD computation, (ii) proposing a dynamic distance bound, which is progressively updated and tightened during the refinement process and can be used to terminate an EMD refinement early, and (iii) proposing a dynamic refinement order for the candidates which, paired with a concurrent EMD refinement strategy, reduces the amount of needless computations. Our proposed techniques are orthogonal to and can be easily integrated with the state-of-the-art filtering techniques, reducing the cost of EMD-based similarity queries by orders of magnitude. / published_or_final_version / Computer Science / Master / Master of Philosophy

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy