Hierarchical Classification and its Application in University Search

Web search engines have been adopted by most universities for searching webpages in their own domains. Basically, a user sends keywords to the search engine and the search engine returns a flat ranked list of webpages. However, in university search, user queries are usually related to topics. Simple keyword queries are often insufficient to express topics as keywords. On the other hand, most E-commerce sites allow users to browse and search products in various hierarchies. It would be ideal if hierarchical browsing and keyword search can be seamlessly combined for university search engines. The main difficulty is to automatically classify and rank a massive number of webpages into the topic hierarchies for universities. In this thesis, we use machine learning and data mining techniques to build a novel hybrid search engine with integrated hierarchies for universities, called SEEU (Search Engine with hiErarchy for Universities). Firstly, we study the problem of effective hierarchical webpage classification. We develop a parallel webpage classification system based on Support Vector Machines. With extensive experiments on the well-known ODP (Open Directory Project) dataset, we empirically demonstrate that our hierarchical classification system is very effective and outperforms the traditional flat classification approaches significantly. Secondly, we study the problem of integrating hierarchical classification into the ranking system of keywords-based search engines. We propose a novel ranking framework, called ERIC (Enhanced Ranking by hIerarchical Classification), for search engines with hierarchies. Experimental results on four large-scale TREC (Text REtrieval Conference) web search datasets show that our ranking system with hierarchical classification outperforms the traditional flat keywords-based search methods significantly. Thirdly, we propose a novel active learning framework to improve the performance of hierarchical classification, which is important for ranking webpages in hierarchies. From our experiments on the benchmark text datasets, we find that our active learning framework can achieve good classification performance yet save a considerable number of labeling effort compared with the state-of-the-art active learning methods for hierarchical text classification. Fourthly, based on the proposed classification and ranking methods, we present a novel hierarchical classification framework for mining academic topics from university webpages. We build an academic topic hierarchy based on the commonly accepted Wikipedia academic disciplines. Based on this hierarchy, we train a hierarchical classifier and apply it to mine academic topics. According to our comprehensive analysis, the academic topics mined by our method are reasonable and consistent with the real-world topic distribution in universities. Finally, we combine all the proposed techniques together and implement the SEEU search engine. According to two usability studies conducted in the ECE and the CS departments at our university, SEEU is favored by the majority of participants. To conclude, the main contribution of this thesis is a novel search engine, called SEEU, for universities. We discuss the challenges toward building SEEU and propose effective machine learning and data mining methods to tackle them. With extensive experiments on well-known benchmark datasets and real-world university webpage datasets, we demonstrate that our system is very effective. In addition, two usability studies of SEEU in our university show that SEEU has a great promise for university search

A New Web Search Engine with Learning Hierarchy

Most of the existing web search engines (such as Google and Bing) are in the form of keyword-based search. Typically, after the user issues a query with the keywords, the search engine will return a flat list of results. When the query issued by the user is related to a topic, only the keyword matching may not accurately retrieve the whole set of webpages in that topic. On the other hand, there exists another type of search system, particularly in e-Commerce web- sites, where the user can search in the categories of different faceted hierarchies (e.g., product types and price ranges). Is it possible to integrate the two types of search systems and build a web search engine with a topic hierarchy? The main diffculty is how to classify the vast number of webpages on the Internet into the topic hierarchy. In this thesis, we will leverage machine learning techniques to automatically classify webpages into the categories in our hierarchy, and then utilize the classification results to build the new search engine SEE. The experimental results demonstrate that SEE can achieve better search results than the traditional keyword-based search engine in most of the queries, particularly when the query is related to a topic. We also conduct a small-scale usability study which further verifies that SEE is a promising search engine. To further improve SEE, we also propose a new active learning framework with several novel strategies for hierarchical classification

Probabilistic Graphical Models for Credibility Analysis in Evolving Online Communities

One of the major hurdles preventing the full exploitation of information from online communities is the widespread concern regarding the quality and credibility of user-contributed content. Prior works in this domain operate on a static snapshot of the community, making strong assumptions about the structure of the data (e.g., relational tables), or consider only shallow features for text classification. To address the above limitations, we propose probabilistic graphical models that can leverage the joint interplay between multiple factors in online communities --- like user interactions, community dynamics, and textual content --- to automatically assess the credibility of user-contributed online content, and the expertise of users and their evolution with user-interpretable explanation. To this end, we devise new models based on Conditional Random Fields for different settings like incorporating partial expert knowledge for semi-supervised learning, and handling discrete labels as well as numeric ratings for fine-grained analysis. This enables applications such as extracting reliable side-effects of drugs from user-contributed posts in healthforums, and identifying credible content in news communities. Online communities are dynamic, as users join and leave, adapt to evolving trends, and mature over time. To capture this dynamics, we propose generative models based on Hidden Markov Model, Latent Dirichlet Allocation, and Brownian Motion to trace the continuous evolution of user expertise and their language model over time. This allows us to identify expert users and credible content jointly over time, improving state-of-the-art recommender systems by explicitly considering the maturity of users. This also enables applications such as identifying helpful product reviews, and detecting fake and anomalous reviews with limited information.Comment: PhD thesis, Mar 201

From Keyword Search to Exploration: How Result Visualization Aids Discovery on the Web

A key to the Web's success is the power of search. The elegant way in which search results are returned is usually remarkably effective. However, for exploratory search in which users need to learn, discover, and understand novel or complex topics, there is substantial room for improvement. Human computer interaction researchers and web browser designers have developed novel strategies to improve Web search by enabling users to conveniently visualize, manipulate, and organize their Web search results. This monograph offers fresh ways to think about search-related cognitive processes and describes innovative design approaches to browsers and related tools. For instance, while key word search presents users with results for specific information (e.g., what is the capitol of Peru), other methods may let users see and explore the contexts of their requests for information (related or previous work, conflicting information), or the properties that associate groups of information assets (group legal decisions by lead attorney). We also consider the both traditional and novel ways in which these strategies have been evaluated. From our review of cognitive processes, browser design, and evaluations, we reflect on the future opportunities and new paradigms for exploring and interacting with Web search results

Multi-facet rating of online hotel reviews: issues, methods and experiments

Online product reviews are becoming increasingly popular, and are being used more and more frequently by consumers in order to choose among competing products. Tools that rank competing products in terms of the satisfaction of consumers that have purchased the product before, are thus also becoming popular. We tackle the problem of rating (i.e., attributing a numerical score of satisfaction to) consumer reviews based on their tex- tual content. In this work we focus on multi-facet rating of hotel reviews, i.e., on the case in which the review of a hotel must be rated several times, according to several aspects (e.g., cleanliness, dining facilities, centrality of location). We explore several aspects of the problem, including the vectorial representation of the text based on sentiment analysis, collocation analysis, and feature selection for ordinal-regression learning. We present the results of experiments conducted on a corpus of approximately 15,000 hotel reviews that we have crawled from a popular hotel review site

Combining granularity-based topic-dependent and topic-independent evidences for opinion detection

Fouille des opinion, une sous-discipline dans la recherche d'information (IR) et la linguistique computationnelle, fait référence aux techniques de calcul pour l'extraction, la classification, la compréhension et l'évaluation des opinions exprimées par diverses sources de nouvelles en ligne, social commentaires des médias, et tout autre contenu généré par l'utilisateur. Il est également connu par de nombreux autres termes comme trouver l'opinion, la détection d'opinion, l'analyse des sentiments, la classification sentiment, de détection de polarité, etc. Définition dans le contexte plus spécifique et plus simple, fouille des opinion est la tâche de récupération des opinions contre son besoin aussi exprimé par l'utilisateur sous la forme d'une requête. Il y a de nombreux problèmes et défis liés à l'activité fouille des opinion. Dans cette thèse, nous nous concentrons sur quelques problèmes d'analyse d'opinion. L'un des défis majeurs de fouille des opinion est de trouver des opinions concernant spécifiquement le sujet donné (requête). Un document peut contenir des informations sur de nombreux sujets à la fois et il est possible qu'elle contienne opiniâtre texte sur chacun des sujet ou sur seulement quelques-uns. Par conséquent, il devient très important de choisir les segments du document pertinentes à sujet avec leurs opinions correspondantes. Nous abordons ce problème sur deux niveaux de granularité, des phrases et des passages. Dans notre première approche de niveau de phrase, nous utilisons des relations sémantiques de WordNet pour trouver cette association entre sujet et opinion. Dans notre deuxième approche pour le niveau de passage, nous utilisons plus robuste modèle de RI i.e. la language modèle de se concentrer sur ce problème. L'idée de base derrière les deux contributions pour l'association d'opinion-sujet est que si un document contient plus segments textuels (phrases ou passages) opiniâtre et pertinentes à sujet, il est plus opiniâtre qu'un document avec moins segments textuels opiniâtre et pertinentes. La plupart des approches d'apprentissage-machine basée à fouille des opinion sont dépendants du domaine i.e. leurs performances varient d'un domaine à d'autre. D'autre part, une approche indépendant de domaine ou un sujet est plus généralisée et peut maintenir son efficacité dans différents domaines. Cependant, les approches indépendant de domaine souffrent de mauvaises performances en général. C'est un grand défi dans le domaine de fouille des opinion à développer une approche qui est plus efficace et généralisé. Nos contributions de cette thèse incluent le développement d'une approche qui utilise de simples fonctions heuristiques pour trouver des documents opiniâtre. Fouille des opinion basée entité devient très populaire parmi les chercheurs de la communauté IR. Il vise à identifier les entités pertinentes pour un sujet donné et d'en extraire les opinions qui leur sont associées à partir d'un ensemble de documents textuels. Toutefois, l'identification et la détermination de la pertinence des entités est déjà une tâche difficile. Nous proposons un système qui prend en compte à la fois l'information de l'article de nouvelles en cours ainsi que des articles antérieurs pertinents afin de détecter les entités les plus importantes dans les nouvelles actuelles. En plus de cela, nous présentons également notre cadre d'analyse d'opinion et tâches relieés. Ce cadre est basée sur les évidences contents et les évidences sociales de la blogosphère pour les tâches de trouver des opinions, de prévision et d'avis de classement multidimensionnel. Cette contribution d'prématurée pose les bases pour nos travaux futurs. L'évaluation de nos méthodes comprennent l'utilisation de TREC 2006 Blog collection et de TREC Novelty track 2004 collection. La plupart des évaluations ont été réalisées dans le cadre de TREC Blog track.Opinion mining is a sub-discipline within Information Retrieval (IR) and Computational Linguistics. It refers to the computational techniques for extracting, classifying, understanding, and assessing the opinions expressed in various online sources like news articles, social media comments, and other user-generated content. It is also known by many other terms like opinion finding, opinion detection, sentiment analysis, sentiment classification, polarity detection, etc. Defining in more specific and simpler context, opinion mining is the task of retrieving opinions on an issue as expressed by the user in the form of a query. There are many problems and challenges associated with the field of opinion mining. In this thesis, we focus on some major problems of opinion mining