An exploratory study on social engagement using Facebook among hotel operators in Malaysia

The use of social media has changed the way communication and interaction with each other and also with business companies such as how hotel industries take place. Currently, social media is the main tool in hotel industry in facilitating the marketing besides being used in engaging with customers. Indeed, recent studies had discovered that customers make decision largely based on the influence of user-generated content and word of mouth. However, there are limited studies which examined the use of Facebook as a tool to engage with customers especially in Malaysia. Thus, this study aims at exploring the use of social media among Malaysian hotels and how these hotels engage with customers through Facebook. Besides, this study also attempts to identify the metrics used by Malaysian hotels to measure the engagement of their customers. Data were collected through interview and content analysis over the Facebook page of selected hotels. The results had indicated that hotels use Facebook as an important communication tool to engage with their customers in various ways. Besides the benefits, this study also discovered some difficulties faced by the hotels when they communicate socially with their customers. On top of that, this study determined that hotels measure their engagement with customers through Facebook in two ways: (i) using software; and (ii) monitoring the page from inside. The results also had shown that some of the hotels are confused about the way to measure the engagement, in which they only focus on certain metrics while others are neglected. In a nutshell, Facebook pages of Malaysian hotels have a relatively weak engagement with customers especially in voice opinion and advocacy. In contrast, attitude expression has a moderate level

Enhanced lexicon based models for extracting question-answer pairs from web forum

A Web forum is an online community that brings people in different geographical locations together. Members of the forum exchange ideas and expertise. As a result, a huge amount of contents on different topics are generated on a daily basis. The huge human generated contents of web forum can be mined as questionanswer pairs (Q&A). One of the major challenges in mining Q&A from web forum is to establish a good relationship between the question and the candidate answers. This problem is compounded by the noisy nature of web forum's human generated contents. Unfortunately, the existing methods that are used to mine knowledge from web forums ignore the effect of noise on the mining tools, making the lexical contents less effective. This study proposes lexicon based models that can automatically mine question-answer pairs with higher accuracy scores from web forum. The first phase of the research produces question mining model. It was implemented using features generated from unigram, bigram, forum metadata and simple rules. These features were screened using both chi-square and wrapper techniques. Wrapper generated features were used by Multinomial Naïve Bayes to finally build the model. The second phase produced a normalized lexical model for answer mining. It was implemented using 13 lexical features that cut across four quality dimensions. The performance of the features was enhanced by noise normalization, a process that fixed orthographic, phonetic and acronyms noises. The third phase of the research produced a hybridized model of lexical and non-lexical features. The average performances of the question mining model, normalized lexical model and hybridized model for answer mining were 90.3%, 97.5%, and 99.5% respectively on three data sets used. They outperformed all previous works in the domain. The first major contribution of the study is the development of an improved question mining model that is characterized by higher accuracy, better specificity, less complex and ability to generate good accuracy across different forum genres. The second contribution is the development of normalized lexical based model that has capability to establish good relationship between a question and its corresponding answer. The third contribution is the development of a hybridized model that integrates lexical features that guarantee relevance with non-lexical that guarantee quality to mine web forum answers. The fourth contribution is a novel integration of question and answer mining models to automatically generate question-answer pairs from web forum

Noise Robust Dialogue Act Recognition for Task-oriented Dialogues

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 이상구.대화 시스템과 이메일, 게시글 요약 시스템 구축에 있어 대화 의도 분류는 중요한 역할을 한다. 이는 각각의 시스템들이 발화, 메일, 게시글 형태의 데이터에 대하여 대화 의도를 분류하고 이 정보를 하위 작업의 입력으로 사용하기 때문이다. 그래서 대화 의도 분류 성능이 해당 시스템 의 전체 성능에 크게 영향을 주기 때문에 성능 향상 측면에 있어 중요하다. 대화 의도 분류는 대화 내 발화에 대화 의도를 할당하는 문제이다. 특히 대화 시스템에서는 음성 인식 에러가 존재하기 때문에 에러에 강인한 대화 의도 분류 모델이 필요하다. 따라서 본 논문에서는 두 명의 사람이 특정 목적을 가지고 진행하는 과제 지향형 대화라는 상황에서 발화, 화자, 대화 의도를 고려하여 대화 구조를 모사하는 생성모델을 만들어 노이즈 데이터에 대응하였다. 이 모델의 기반이 되는 가정은 화자는 어떠한 행위를 수행하고자 하는 목적을 가지고, 그 목적에 맞는 적절한 어휘 집합을 사용하여 상대방에게 말을 한다는 것이다. 즉 제안한 모델은 이러한 가정을 고려하여 마르코프 모델을 개선하였다. 과제 지향형 데이터인 HCRC map task, live chat, SACTI-1 말뭉치를 이용한 실험을 통해 제안한 모델이 기존 마르코프 모델에 비하여 더 나은 성능을 보이고, 현재까지도 대화 의도 분류 성능이 높은 SVM-HMM과 경쟁력 있는 결과를 보이는 것을 확인 하였다. 특히 대화 시스템의 음성 인식 모듈의 에러를 모방한 SACTI-1 말뭉치에 대하여 제안한 모델이 SVM-HMM에 비하여 노이즈에 강인함을 보였다.In spoken dialog system, e-mail summary system and thread summary system development, dialogue act classifier plays an important role because the systems depend on the performance of classifying dialogue acts of utterances, e-mails and posts to improve completeness of the system. The dialogue act classification problem is a well-known problem to assign the dialogue acts to utterances in a conversation. One of the main challenges in the development of robust dialog systems is especially to deal with noisy input due to imperfect results from Automatic Speech Recognition (ASR) module. The challenge in dialogue act recognition is the mapping from noisy user utterances to dialogue acts. In this paper, to cope with noisy utterances, we describe a noise robust generative model of task-oriented conversation that captures both the speaker information and the dialogue act associated with each utterance under the assumption that a speaker says about something by using appropriate vocabulary with the aim of getting someone to do somethings. The proposed model is based on Markov model and is modified to reflect the assumption. In the experiments, we evaluate the classification results by comparing them to the simple Markov model and state-of-the-art SVM-HMM results. The proposed model is a better conversation model than the simple Markov model and shows the competitive classification results in comparison with SVM-HMM in the task-oriented HCRC map task corpus, live-chat corpus and SACTI-1 corpus. Results based on SACTI-1 corpus which simulates ASR errors particularly show that the proposed model is robust against noisy user utterances.1. 서론 1 1.1 연구의 배경 1 1.2 연구의 내용 및 범위 3 1.3 논문의 구성 6 2. 문제 정의 7 2.1 대화문의 구성요소 7 2.2 대화 의도 분류 문제 정의 12 2.3 대화문의 특징 및 문제 해결의 어려운 점 13 3. 관련 연구 15 3.1 지도 학습 기반의 대화 의도 분류 연구 15 3.2 대화 의도의 의존 관계를 모델링 한 연구 16 3.3 기존 연구의 한계점 22 4. 마르코프 모델 기반 대화 의도 분류 24 4.1 배경지식 24 4.1.1 언어모델 24 4.1.2 마르코프 모델과 은닉 마르코프 모델 25 4.2 입출력 마르코프 모델을 변형한 대화 의도 분류 모델 26 5. 성능 평가 31 5.1 대화 말뭉치 31 5.2 비교모델 및 개발환경 38 5.3 성능 평가 측정치 39 5.4 실험 결과 및 분석 40 5.4.1 분류 성능 41 5.4.2 ASR 노이즈에 대한 강인성 45 5.4.3 확장성 48 6. 결론 및 향후 연구 50 6.1 결론 50 6.2 향후 연구 51 참고문헌 53 ABSTRACT 57Maste

Computational Argumentation Approaches to Improve Sensemaking and Evidence-based Reasoning in Online Deliberation Systems

Deliberation is the process through which communities identify potential solutions for a problem and select the solution that most effectively meets their diverse requirements through dialogic communication. Online deliberation is implemented nowadays with means of social media and online discussion platforms; however, these media present significant challenges and issues that can be traced to inadequate support for Sensemaking processes and poor endorsement of the quality characteristics of deliberation. This thesis investigates integrating computational argumentation methods in online deliberation platforms as an effective way to improve participants' perception of the quality of the deliberation process, their way of making sense of the overall process and producing healthier social dynamics. For that, two computational artefacts are proposed: (i) a Synoptical summariser of long discussions and (ii) a Scientific Argument Recommender System (SciArgRecSys). The two artefacts are designed and developed with state-of-the-art methods (with the use of Large Language Models - LLMs) and evaluated intrinsically and extrinsically when deployed in a real live platform (BCause). Through extensive evaluation, the positive effect of both artefacts is illustrated in human Sensemaking and essential quality characteristics of deliberation such as reciprocal Engagement, Mutual Understanding, and Social dynamics. In addition, it has been demonstrated that these interventions effectively reduce polarisation, the formation of sub-communities while significantly enhancing the quality of the discussion by making it more coherent and diverse

Analytics-based approach to the study of learning networks in digital education settings

Investigating howgroups communicate, build knowledge and expertise, reach consensus or collaboratively solve complex problems, became one of the main foci of contemporary research in learning and social sciences. Emerging models of communication and empowerment of networks as a form of social organization further reshaped practice and pedagogy of online education, bringing research on learning networks into the mainstream of educational and social science research. In such conditions, massive open online courses (MOOCs) emerged as one of the promising approaches to facilitating learning in networked settings and shifting education towards more open and lifelong learning. Nevertheless, this most recent educational turn highlights the importance of understanding social and technological (i.e., material) factors as mutually interdependent, challenging the existing forms of pedagogy and practice of assessment for learning in online environments. On the other hand, the main focus of the contemporary research on networked learning is primarily oriented towards retrospective analysis of learning networks and informing design of future tasks and recommendations for learning. Although providing invaluable insights for understanding learning in networked settings, the nature of commonly applied approaches does not necessarily allow for providing means for understanding learning as it unfolds. In that sense, learning analytics, as a multidisciplinary research field, presents a complementary research strand to the contemporary research on learning networks. Providing theory-driven and analytics-based methods that would allow for comprehensive assessment of complex learning skills, learning analytics positions itself either as the end point or a part of the pedagogy of learning in networked settings. The thesis contributes to the development of learning analytics-based research in studying learning networks that emerge fromthe context of learning with MOOCs. Being rooted in the well-established evidence-centered design assessment framework, the thesis develops a conceptual analytics-based model that provides means for understanding learning networks from both individual and network levels. The proposed model provides a theory-driven conceptualization of the main constructs, along with their mutual relationships, necessary for studying learning networks. Specifically, to provide comprehensive understanding of learning networks, it is necessary to account for structure of learner interactions, discourse generated in the learning process, and dynamics of structural and discourse properties. These three elements – structure, discourse, and dynamics – should be observed as mutually dependent, taking into account learners’ personal interests, motivation, behavior, and contextual factors that determine the environment in which a specific learning network develops. The thesis also offers an operationalization of the constructs identified in the model with the aim at providing learning analytics-methods for the implementation of assessment for learning. In so doing, I offered a redefinition of the existing educational framework that defines learner engagement in order to account for specific aspects of learning networks emerging from learning with MOOCs. Finally, throughout the empirical work presented in five peer-reviewed studies, the thesis provides an evaluation of the proposed model and introduces novel learning analytics methods that provide different perspectives for understanding learning networks. The empirical work also provides significant theoretical and methodological contributions for research and practice in the context of learning networks emerging from learning with MOOCs