Modèles de langues pour la détection d'opinions dans les blogs

Cet article décrit une approche de recherche de documents pertinents vis-à-vis d’une requête et exprimant une opinion. Afin de détecter si un document est porteur d’opinion (i.e. comporte de l’information subjective), nous proposons de le comparer à des sources d’information qui comportent du contenu de type opinion. L’intuition derrière cela est la suivante : un document ayant une similarité forte avec des sources d’opinions, est vraisemblablement porteur d’opinion. Pour mesurer cette similarité, nous exploitons des modèles de langue. Nous modélisons le document et la source (référence) porteuse d’opinions par des modèles de langue, nous évaluons ensuite la similarité de ces modèles. Plusieurs expérimentations ont été réalisées sur des collections issues de TREC. Les résultats obtenus valident notre intuition

Methods for ranking user-generated text streams: a case study in blog feed retrieval

User generated content are one of the main sources of information on the Web nowadays. With the huge amount of this type of data being generated everyday, having an efficient and effective retrieval system is essential. The goal of such a retrieval system is to enable users to search through this data and retrieve documents relevant to their information needs. Among the different retrieval tasks of user generated content, retrieving and ranking streams is one of the important ones that has various applications. The goal of this task is to rank streams, as collections of documents with chronological order, in response to a user query. This is different than traditional retrieval tasks where the goal is to rank single documents and temporal properties are less important in the ranking. In this thesis we investigate the problem of ranking user-generated streams with a case study in blog feed retrieval. Blogs, like all other user generated streams, have specific properties and require new considerations in the retrieval methods. Blog feed retrieval can be defined as retrieving blogs with a recurrent interest in the topic of the given query. We define three different properties of blog feed retrieval each of which introduces new challenges in the ranking task. These properties include: 1) term mismatch in blog retrieval, 2) evolution of topics in blogs and 3) diversity of blog posts. For each of these properties, we investigate its corresponding challenges and propose solutions to overcome those challenges. We further analyze the effect of our solutions on the performance of a retrieval system. We show that taking the new properties into account for developing the retrieval system can help us to improve state of the art retrieval methods. In all the proposed methods, we specifically pay attention to temporal properties that we believe are important information in any type of streams. We show that when combined with content-based information, temporal information can be useful in different situations. Although we apply our methods to blog feed retrieval, they are mostly general methods that are applicable to similar stream ranking problems like ranking experts or ranking twitter users

Local Information Diffusion Patterns in Social and Traditional Media: The Estonian Case Study

Paljud ettevõtted ja inimesed hindavad kõrgelt informatsiooni väärtust ja seda on eelkõige hakatud hindama viimase kümnekonna aasta jooksul. Tänu sellele on tekkinud ka huvi, kuidas info levib erinevates struktureeritud võrgustikes. Avaldatud on mitmeid teadustöid, mis uurivad informatsiooni levimist ühes reaalse elu võrgustikus nagu näiteks Facebooki postitused, Twitteri tweetid, Blogspoti blogikanded jne. Suuresti on need uurimused keskendunud ühele võrgustikule, mis ei hõlma kogu võrgu dünaamikat ja samuti välist mõju info levimisele. Samas on lähiminevikus avaldatud ka teadustöid, mis hõlmavad mitut erinevat võrgustiku ja analüüsivad välist mõju informatsiooni levimisele. Käesoleva töö eesmärk on lähemalt uurida informatsiooni levimise mustreid võrgustikus, mis hõlmab erinevaid reaalelu võrgustike, kasutades selleks topoloogilisi ja aja mustreid. Topoloogiliste mustrite analüüsimiseks on kasutatud võrgustikus sagedalt levivate alamgraafide leidmise algoritme, aja mustreid uuritakse ajaseeriate klasterdamise teel. Töös kasutatud andmestik on kogutud Eesti uudismeediast - artiklid ja nende kommentaarid ning sotsiaalmeedia kanalitest, Twitterist ja Facebook-ist. Selle andmestiku põhjal loodi seosed eritüüpi andmeobjektide vahel, mille põhjal loodi võrgustik, mida kasutada edasiseks uurimiseks. Aja mustrid viitavad väga kiirele info levimisele antud võrgustikus, topoloogilised mustrid näitavad uudismeedia artiklite ja Facebook-i postituste suurt mõju info levimises. Töö tulemusi on võimalik rakendada küberkaitses, online turunduses ja kampaania haldamises, samuti ka mõjuvõimu hindamisel - kindlasti leiaks tulemused rakendust ka teistes valdkondades.Information has become more highly valued among companies and individuals than ever before. With this, the interest in how information diffuses among the entities in various structured networks has increased. A number of studies have been published on the diffusion process in real-life networks, such as web service network, citation networks, blog networks etc. Majority of researches have focused on one type of network - such as Facebook posts, Twitter tweets, Blogspot blog entries etc. A disadvantage of analysing a network containing entities from a single source is that it does not consider the outside influence on the diffusion. Recently, some papers have started to incorporate different networks in their study and as such have been able to analyse the effect of outside influence on the diffusion process. This thesis aims to shed further light into the topic of information diffusion in a real world network containing entities from different sources, this is achieved by detection of relevant local topological and temporal information diffusion patterns. For topological pattern analysis, frequent subgraph mining techniques are used. Temporal patterns are extracted using time series clustering. The dataset used in this thesis is collected from the Estonian setting of mainstream online news media with comments and articles and from social media channels Twitter and Facebook. From this dataset the relations between the entities were extracted and a network for analysis of diffusion patterns was constructed. Temporal patterns reveal the high pace of information diffusion while topological patterns expose the important role of news media articles and Facebook posts in the information diffusion processes. The results of the thesis are applicable in cyber defence, online marketing and campaign management plus information impact estimation, just to mention a few application areas

Utilizing Multi-modal Weak Signals to Improve User Stance Inference in Social Media

Social media has become an integral component of the daily life. There are millions of various types of content being released into social networks daily. This allows for an interesting view into a users\u27 view on everyday life. Exploring the opinions of users in social media networks has always been an interesting subject for the Natural Language Processing researchers. Knowing the social opinions of a mass will allow anyone to make informed policy or marketing related decisions. This is exactly why it is desirable to find comprehensive social opinions. The nature of social media is complex and therefore obtaining the social opinion becomes a challenging task. Because of how diverse and complex social media networks are, they typically resonate with the actual social connections but in a digital platform. Similar to how users make friends and companions in the real world, the digital platforms enable users to mimic similar social connections. This work mainly looks at how to obtain a comprehensive social opinion out of social media network. Typical social opinion quantifiers will look at text contributions made by users to find the opinions. Currently, it is challenging because the majority of users on social media will be consuming content rather than expressing their opinions out into the world. This makes natural language processing based methods impractical due to not having linguistic features. In our work we look to improve a method named stance inference which can utilize multi-domain features to extract the social opinion. We also introduce a method which can expose users opinions even though they do not have on-topical content. We also note how by introducing weak supervision to an unsupervised task of stance inference we can improve the performance. The weak supervision we bring into the pipeline is through hashtags. We show how hashtags are contextual indicators added by humans which will be much likelier to be related than a topic model. Lastly we introduce disentanglement methods for chronological social media networks which allows one to utilize the methods we introduce above to be applied in these type of platforms

Exploring Automated Code Evaluation Systems and Resources for Code Analysis: A Comprehensive Survey

The automated code evaluation system (AES) is mainly designed to reliably assess user-submitted code. Due to their extensive range of applications and the accumulation of valuable resources, AESs are becoming increasingly popular. Research on the application of AES and their real-world resource exploration for diverse coding tasks is still lacking. In this study, we conducted a comprehensive survey on AESs and their resources. This survey explores the application areas of AESs, available resources, and resource utilization for coding tasks. AESs are categorized into programming contests, programming learning and education, recruitment, online compilers, and additional modules, depending on their application. We explore the available datasets and other resources of these systems for research, analysis, and coding tasks. Moreover, we provide an overview of machine learning-driven coding tasks, such as bug detection, code review, comprehension, refactoring, search, representation, and repair. These tasks are performed using real-life datasets. In addition, we briefly discuss the Aizu Online Judge platform as a real example of an AES from the perspectives of system design (hardware and software), operation (competition and education), and research. This is due to the scalability of the AOJ platform (programming education, competitions, and practice), open internal features (hardware and software), attention from the research community, open source data (e.g., solution codes and submission documents), and transparency. We also analyze the overall performance of this system and the perceived challenges over the years

The voting model for people search

The thesis investigates how persons in an enterprise organisation can be ranked in response to a query, so that those persons with relevant expertise to the query topic are ranked first. The expertise areas of the persons are represented by documentary evidence of expertise, known as candidate profiles. The statement of this research work is that the expert search task in an enterprise setting can be successfully and effectively modelled using a voting paradigm. In the so-called Voting Model, when a document is retrieved for a query, this document represents a vote for every expert associated with the document to have relevant expertise to the query topic. This voting paradigm is manifested by the proposition of various voting techniques that aggregate the votes from documents to candidate experts. Moreover, the research work demonstrates that these voting techniques can be modelled in terms of a Bayesian belief network, providing probabilistic semantics for the proposed voting paradigm. The proposed voting techniques are thoroughly evaluated on three standard expert search test collections, deriving conclusions concerning each component of the Voting Model, namely the method used to identify the documents that represent each candidate's expertise areas, the weighting models that are used to rank the documents, and the voting techniques which are used to convert the ranking of documents into the ranking of experts. Effective settings are identified and insights about the behaviour of each voting technique are derived. Moreover, the practical aspects of deploying an expert search engine such as its efficiency and how it should be trained are also discussed. This thesis includes an investigation of the relationship between the quality of the underlying ranking of documents and the resulting effectiveness of the voting techniques. The thesis shows that various effective document retrieval approaches have a positive impact on the performance of the voting techniques. Interestingly, it also shows that a `perfect' ranking of documents does not necessarily translate into an equally perfect ranking of candidates. Insights are provided into the reasons for this, which relate to the complexity of evaluating tasks based on ranking aggregates of documents. Furthermore, it is shown how query expansion can be adapted and integrated into the expert search process, such that the query expansion successfully acts on a pseudo-relevant set containing only a list of names of persons. Five ways of performing query expansion in the expert search task are proposed, which vary in the extent to which they tackle expert search-specific problems, in particular, the occurrence of topic drift within the expertise evidence for each candidate. Not all documentary evidence of expertise for a given person are equally useful, nor may there be sufficient expertise evidence for a relevant person within an enterprise. This thesis investigates various approaches to identify the high quality evidence for each person, and shows how the World Wide Web can be mined as a resource to find additional expertise evidence. This thesis also demonstrates how the proposed model can be applied to other people search tasks such as ranking blog(ger)s in the blogosphere setting, and suggesting reviewers for the submitted papers to an academic conference. The central contributions of this thesis are the introduction of the Voting Model, and the definition of a number of voting techniques within the model. The thesis draws insights from an extremely large and exhaustive set of experiments, involving many experimental parameters, and using different test collections for several people search tasks. This illustrates the effectiveness and the generality of the Voting Model at tackling various people search tasks and, indeed, the retrieval of aggregates of documents in general

News vertical search using user-generated content

The thesis investigates how content produced by end-users on the World Wide Web — referred to as user-generated content — can enhance the news vertical aspect of a universal Web search engine, such that news-related queries can be satisfied more accurately, comprehensively and in a more timely manner. We propose a news search framework to describe the news vertical aspect of a universal web search engine. This framework is comprised of four components, each providing a different piece of functionality. The Top Events Identification component identifies the most important events that are happening at any given moment using discussion in user-generated content streams. The News Query Classification component classifies incoming queries as news-related or not in real-time. The Ranking News-Related Content component finds and ranks relevant content for news-related user queries from multiple streams of news and user-generated content. Finally, the News-Related Content Integration component merges the previously ranked content for the user query into theWeb search ranking. In this thesis, we argue that user-generated content can be leveraged in one or more of these components to better satisfy news-related user queries. Potential enhancements include the faster identification of news queries relating to breaking news events, more accurate classification of news-related queries, increased coverage of the events searched for by the user or increased freshness in the results returned. Approaches to tackle each of the four components of the news search framework are proposed, which aim to leverage user-generated content. Together, these approaches form the news vertical component of a universal Web search engine. Each approach proposed for a component is thoroughly evaluated using one or more datasets developed for that component. Conclusions are derived concerning whether the use of user-generated content enhances the component in question using an appropriate measure, namely: effectiveness when ranking events by their current importance/newsworthiness for the Top Events Identification component; classification accuracy over different types of query for the News Query Classification component; relevance of the documents returned for the Ranking News-Related Content component; and end-user preference for rankings integrating user-generated content in comparison to the unalteredWeb search ranking for the News-Related Content Integration component. Analysis of the proposed approaches themselves, the effective settings for the deployment of those approaches and insights into their behaviour are also discussed. In particular, the evaluation of the Top Events Identification component examines how effectively events — represented by newswire articles — can be ranked by their importance using two different streams of user-generated content, namely blog posts and Twitter tweets. Evaluation of the proposed approaches for this component indicates that blog posts are an effective source of evidence to use when ranking events and that these approaches achieve state-of-the-art effectiveness. Using the same approaches instead driven by a stream of tweets, provide a story ranking performance that is significantly more effective than random, but is not consistent across all of the datasets and approaches tested. Insights are provided into the reasons for this with regard to the transient nature of discussion in Twitter. Through the evaluation of the News Query Classification component, we show that the use of timely features extracted from different news and user-generated content sources can increase the accuracy of news query classification over relying upon newswire provider streams alone. Evidence also suggests that the usefulness of the user-generated content sources varies as news events mature, with some sources becoming more influential over time as new content is published, leading to an upward trend in classification accuracy. The Ranking News-Related Content component evaluation investigates how to effectively rank content from the blogosphere and Twitter for news-related user queries. Of the approaches tested, we show that learning to rank approaches using features specific to blog posts/tweets lead to state-of-the-art ranking effectiveness under real-time constraints. Finally this thesis demonstrates that the majority of end-users prefer rankings integrated with usergenerated content for news-related queries to rankings containing only Web search results or integrated with only newswire articles. Of the user-generated content sources tested, the most popular source is shown to be Twitter, particularly for queries relating to breaking events. The central contributions of this thesis are the introduction of a news search framework, the approaches to tackle each of the four components of the framework that integrate user-generated content and their subsequent evaluation in a simulated real-time setting. This thesis draws insights from a broad range of experiments spanning the entire search process for news-related queries. The experiments reported in this thesis demonstrate the potential and scope for enhancements that can be brought about by the leverage of user-generated content for real-time news search and related applications