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Review helpfulness prediction has attracted growing attention of researchers that proposed various solutions using Machine Learning (ML) techniques. Most of the studies used online reviews from Amazon to predict helpfulness where each review is accompanied with information indicating how many people found the review helpful. This research aims to analyze the complete process of modelling review helpfulness from several perspectives. Experiments are conducted comparing different methods for representing the review text as well as analyzing the importance of data sampling for regression compared to using non-sampled datasets. Additionally, a set of review, review meta-data and product features are evaluated on their ability to capture the helpfulness of reviews. Two Amazon product review datasets are utilized for the experiments and two of the most widely used machine-learning algorithms, Linear Regression and Convolutional Neural Network (CNN). The experiments empirically demonstrate that the choice of representation of the textual data has an impact on performance with tf-idf and word2Vec obtaining the lowest Mean Squared Error (MSE) values. The importance of data sampling is also evident from the experiments as the imbalanced ratios in the unsampled dataset negatively affected the performance of both models with bias predictions in favor of the majority group of high ratios in the dataset. Lastly, the findings suggest that review features such as unigrams of review text and title, length of review text in words, polarity of title along with rating as review meta-data feature are the most influential features for determining helpfulness of reviews.

The purpose of this study was to explore sex differences in the use and evaluated helpfulness of advice received before marriage. In addition, this study explored who typically gave premarital advice. Advice is considered by some to be a form of social support which can be helpful or hurtful to the marriage relationship. The sex of the advice-giver and advice-receiver as well as the relationship quality between them was explored in order to highlight how these variables affect advice use and helpfulness. Utilizing a questionnaire and interviews with individual newlywed husbands (n = 56) and wives (n = 56), data were collected and analyzed. Advice was mostly given by mothers, fathers, friends, and religious leaders. Generally, both husbands and wives used the advice they were given and both evaluated the advice as helpful. Sex did not have a significant impact on advice use or helpfulness, but in some cases, the relationship between the advice-giver and advice-receiver significantly influenced the use and evaluated helpfulness of advice.

Little is known about the role that personality and emotional readiness factors may play in participation and outcomes of premarital education programs in varying formats. Data collected via the RELATionship Evaluation Questionnaire (RELATE: Busby et al., 2001) was used to analyze how personality and emotional readiness factors affect perceived change and helpfulness in self-directed and workshop formats of premarital education for 384 individuals who participated in such interventions. Depression was significantly and negatively related to participant perception of positive change and helpfulness in a workshop setting. Kindness was positively and significantly related to perceived positive change in both workshop and self-directed formats, and income was negatively and significantly related to perceived positive change in workshop settings. Anxiety was significantly and positively related to perceived helpfulness in workshop settings. Implications of these findings are discussed. More research is needed to compare these results to other formats of premarital interventions, such as classes and counseling formats, and to more diverse population samples.

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More and more in recent years, activities that people once did in the real world they now do in virtual space. In particular, online communities have become popular and efficient media for people all over the world to seek and share knowledge in domains that interest them. Such communities are called online knowledge communities (OKCs). Large-scale OKCs may comprise thousands of community members and archive many  more online messages. As a result, problems such as how to identify and manage the knowledge collected and how to understand people\'s knowledge-sharing behaviors have become major challenges for leveraging online knowledge to sustain community growth. In this dissertation I examine three important factors of managing knowledge in OKCs. First, I focus on how to build successful profiles for community members that describe their domain expertise. These expertise profiles are potentially important for directing questions to the right people and, thus, can improve the community\'s overall efficiency and efficacy. To address this issue, I present a comparative study of models of expertise profiling in online communities and identify the model combination that delivers the best results. Next, I investigate how to automatically assess the information helpfulness of user postings. Due to the voluntary nature of online participation, there is no guarantee that all user-generated content (UGC) will be helpful. It is also difficult, given the sheer amount of online postings, for knowledge seekers to find information quickly that satisfies their informational needs. Therefore, I propose a theory-driven text classification framework based on the knowledge adoption model (KAM) for predicting the helpfulness of UGC in OKCs. I test the effectiveness of this framework at both the thread level and the post level of online messages. Any given OKC generally has a huge number of individuals participating in online discussions, but exactly what, where, when and how they seek and share knowledge are still not fully understood or documented. In the last part of the dissertation, I describe a multi-level study of the knowledge-sharing behaviors of users in OKCs. Both exploratory data analysis and network analysis are applied to thread, forum and community levels of online data. I present a number of interesting findings on social dynamics in knowledge sharing and diffusion. These findings potentially have important implications for both the theory and practice of online community knowledge management. / Ph. D.

Online customer reviews are web content voluntarily posted by the users of a product (e.g. camera) or service (e.g. hotel) to express their opinions about the product or service. Online reviews are important resources for businesses and consumers. This dissertation focuses on the important consumer concern of review utility, i.e., the helpfulness or usefulness of online reviews to inform consumer purchase decisions. Review utility concerns consumers since not all online reviews are useful or helpful. And, the quantity of the online reviews of a product/service tends to be very large. Manual assessment of review utility is not only time consuming but also information overloading. To address this issue, review helpfulness research (RHR) has become a very active research stream dedicated to study utility-sensitive review analysis (USRA) techniques for automating review utility assessment. Unfortunately, prior RHR solution is inadequate. RHR researchers call for more suitable USRA approaches. Our current research responds to this urgent call by addressing the research problem: What is an adequate USRA approach? We address this problem by offering novel Design Science (DS) artifacts for personalized USRA (PUSRA). Our proposed solution extends not only RHR research but also web personalization research (WPR), which studies web-based solutions for personalized web provision. We have evaluated the proposed solution by applying three evaluation methods: analytical, descriptive, and experimental. The evaluations corroborate the practical efficacy of our proposed solution. This research contributes what we believe (1) the first DS artifacts to the knowledge body of RHR and WPR, and (2) the first PUSRA contribution to USRA practice. Moreover, we consider our evaluations of the proposed solution the first comprehensive assessment of USRA solutions. In addition, this research contributes to the advancement of decision support research and practice. The proposed solution is a web-based decision support artifact with the capability to substantially improve accurate personalized webpage provision. Also, website designers can apply our research solution to transform their works fundamentally. Such transformation can add substantial value to businesses.

La presente investigación es de tipo descriptiva y explicativa, donde se propone una solución geotécnica para el reforzamiento de un talud aplicando el sistema terramesh, para lo cual se aplica un cálculo de estabilidad que determina factores de seguridad y superficies de falla del suelo, a su vez se obtendrá parámetros geotécnicos mediante ensayos de laboratorio. Se aplicará un método de estabilidad, siendo este el método de introducción de fuerzas resistentes del tipo terramesh. Asimismo utilizar el software MacStars 2000 como herramienta para el dimensionamiento y diseño del muro de suelo reforzado tipo terramesh para ser aplicado en el talud de la carretera, el cual tendrá un mínimo impacto ambiental, devolviendo la comunicación y servicialidad de la vía para los pueblos aledaños. Finalmente determinar las ventajas y desventajas que proporciona la solución. The present research is descriptive and explanatory type where a geotechnical solution strengthening a batter applying Terramesh system, for which stability calculations determining safety factors and failure surfaces of soil applied, proposing to his Once geotechnical parameters are obtained through laboratory testing. A method of stability will apply, which is the input method forces Terramesh resistant type. 2000 MacStars also use software as a tool for sizing and design of reinforced soil wall Terramesh type to be applied on the slope of the road, which will have a minimal environmental impact, restoring communication and helpfulness of the way to the surrounding town. Finally determine the advantages and disadvantages that provides the solution.

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在電子商務中，評論會影響消費者的購買決策，透過評論幫助性可以篩選出關鍵的評論，以利消費者進行決策。本研究以推敲可能性模式作為研究架構，透過文字探勘挖掘評論的文本特性來探討影響幫助性之要素，中央線索除了評論長度與可讀性外，利用LDA主題模型衡量評論主題廣度；周邊線索則是透過環狀情緒模型進行情感分析，並透過評論者排名來衡量來源可信度，利用亞馬遜商店中的資料進行驗證分析。結果發現，消費者在判斷評論幫助性時，會參考中央以及周邊線索。具備高論點品質的中央線索將有效提升評論幫助性；周邊線索整體而言，證實了社會中存在負向偏誤，具備喚起度的負向情感較容易提升評論幫助性，而評論是否被認為有幫助確實會受到評論者的排名所影響。進階分析結果顯示，周邊的情感效果會受到評論者排名高低的影響，前段評論者應保持中立避免帶有個人情緒；中段評論者的評論幫助性會隨著情緒喚起度而增加；後段評論者則需要增加自身的負向情感，才能夠對於評論幫助性有正向影響。 / Online reviews are important factors in consumers’ purchase decision. The helpfulness of reviews allows consumers to quickly identify useful reviews. The purpose of this study is to investigate the nature of online reviews that affect their helpfulness through the lens of the elaboration likelihood model. For the central cues, we adopt latent dirichlet allocation to measure review breadth in addition to review length and review readability. For the peripheral cues, we use the sentiment analysis based on the circumplex model to catch the emotion effect and use the ranking of the reviewers to measure the source credibility. We used a dataset collected from Amazon.com to evaluate our model. The result suggests that consumers focus both central and peripheral cues when they read reviews. Consumers care about the length, breadth and readability of reviews associated with the central route, and the emotional effects associated with the peripheral route. In the advanced research, we split our sample into 3 groups by their ranking of the reviewers. We found that the top reviewers should keep neutral and avoid personal feelings to make their reviews more helpful; the middle reviewers can use more arousal words to improve their review helpfulness; the bottom reviewers must increase their emotional valence strength, especially the negative emotion to higher the perceived review helpfulness.