An evaluation of the role of sentiment in second screen microblog search tasks

The recent prominence of the real-time web is proving both challenging and disruptive for information retrieval and web data mining research. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user's query at a point in time, automated methods are required to sift through this information. Sentiment analysis offers a promising direction for modelling microblog content. We build and evaluate a sentiment-based filtering system using real-time user studies. We find a significant role played by sentiment in the search scenarios, observing detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users' prior topic sentiment

Developing an Ontological approach to Content-based Recommendation System

During past decade the no of web user increases so rapidly leading to rapid increase in web services which leads to increase the usage data at higher rate. The usage data of these users now amount to be in order of Peta Byte (1015 bytes). In such cases the search space for user‟s queries increases and a user‟s search query may leads to retrieval of irrelevant information. Sometime the search algorithm may become exhaustive. This project is aimed to use User‟s context information to model a framework which can filter the search space and choose some preference based on user‟s context. The project follows a set of processes for profile construction of Users and Items, and determining their similarity and scoring their preference. The projects also compares the effectiveness in predicting User‟s preferences and accuracy in it with various other Collaborative approach such as User-Based model and Item based model to check its performance level and quality of predictions

Aeronautical Information Geoservices

Aeronautical charts underlie the representation of aeronautic geographic information that supports pilots in flight. Nevertheless, charts become complex due to the high density of data and the different kinds that support each phase of flight. These features make difficult using them on board. After conducting a study that aims to understand and to evaluate pilot’s needs related to Geographic Information, it is proposed a solution to implement a platform based on geographic information standards (OGC, ISO) and supported by a distributed Web architecture. This platform facilitates the use, retrieval, updating of information and its exchange among different institutions through private and public users. As a first element to ensure interoperability and the harmonisation of information, we propose an aeronautical metadata profile that sets guidelines and elements for its description. This profile meets the standards set by ICAO, Eurocontrol and ISO. The platform offers three levels of access to data through different types of devices and user profiles. This paper suggests an alternative and reliable way for distributing aeronautical geoinformation, focusing on specific functions or displaying and querying

Sentiment analysis and real-time microblog search

This thesis sets out to examine the role played by sentiment in real-time microblog search. The recent prominence of the real-time web is proving both challenging and disruptive for a number of areas of research, notably information retrieval and web data mining. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user query at a given point in time, automated methods are required to enable users to sift through this information. As an area of research reaching maturity, sentiment analysis offers a promising direction for modelling the text content in microblog streams. In this thesis we review the real-time web as a new area of focus for sentiment analysis, with a specific focus on microblogging. We propose a system and method for evaluating the effect of sentiment on perceived search quality in real-time microblog search scenarios. Initially we provide an evaluation of sentiment analysis using supervised learning for classi- fying the short, informal content in microblog posts. We then evaluate our sentiment-based filtering system for microblog search in a user study with simulated real-time scenarios. Lastly, we conduct real-time user studies for the live broadcast of the popular television programme, the X Factor, and for the Leaders Debate during the Irish General Election. We find that we are able to satisfactorily classify positive, negative and neutral sentiment in microblog posts. We also find a significant role played by sentiment in many microblog search scenarios, observing some detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users’ prior topic sentiment

An adaptive meta-search engine considering the user’s field of interest

AbstractExisting meta-search engines return web search results based on the page relevancy to the query, their popularity and content. It is necessary to provide a meta-search engine capable of ranking results considering the user’s field of interest. Social networks can be useful to find the users’ tendencies, favorites, skills, and interests. In this paper we propose MSE, a meta-search engine for document retrieval utilizing social information of the user. In this approach, each user is assumed to have a profile containing his fields of interest. MSE extracts main phrases from the title and short description of receiving results from underlying search engines. Then it clusters the main phrases by a Self-Organizing Map neural network. Generated clusters are then ranked on the basis of the user’s field of interest. We have compared the proposed MSE against two other meta-search engines. The experimental results show the efficiency and effectiveness of the proposed method

Geoservices for Aeronautical Navigation

Aeronautical charts underlie the representation of aeronautic geographic information that supports pilots in flight. Nevertheless, the charts become complex due to the high density of data and the different kinds of charts that support each phase of flight. These features make difficult using them on board. After conducting a study, with civil Spaniard pilots, that aims to understand and to evaluate their needs related to Geographic Information, it is proposed a solution to implement a platform based on geographic information standards (OGC, ISO) and supported by a distributed Web architecture. This platform facilitates the use, retrieval, updating of information and its exchange among different institutions through private and public users. As a first element to ensure interoperability of information, we suggest an aeronautical metadata profile that sets guidelines and elements for its description. The metadata profile meets the standards set by ICAO, Eurocontrol and ISO. The platform offers three levels of access to data through different types of devices and user profiles. Thus, aeronautical institutions could edit data while pilot is on board accessing digital aeronautical charts through a laptop or Table PC. This paper suggests an alternative and reliable way for distributing aeronautical geoinformation, focusing on specific functions or displaying and querying

Hybrid Profiling in Information Retrieval

Abstract-One of the main challenges in search engine quality of service is how to satisfy the needs and the interests of individual users. This raises the fundamental issue of how to identify and select the information that is relevant to a specific user. This concern over generic provision and the lack of search precision have provided the impetus for the research into Web Search personalisation. In this paper a hybrid user profiling system is proposed -a combination of explicit and implicit user profiles for improving the web search effectiveness in terms of precision and recall. The proposed system is content-based and implements the Vector Space Model. Experimental results, supported by significance tests, indicate that the system offers better precision and recall in comparison to traditional search engines

PRESY: A Context Based Query Reformulation Tool for Information Retrieval on the Web

Problem Statement: The huge number of information on the web as well as the growth of new inexperienced users creates new challenges for information retrieval. It has become increasingly difficult for these users to find relevant documents that satisfy their individual needs. Certainly the current search engines (such as Google, Bing and Yahoo) offer an efficient way to browse the web content. However, the result quality is highly based on uses queries which need to be more precise to find relevant documents. This task still complicated for the majority of inept users who cannot express their needs with significant words in the query. For that reason, we believe that a reformulation of the initial user's query can be a good alternative to improve the information selectivity. This study proposes a novel approach and presents a prototype system called PRESY (Profile-based REformulation SYstem) for information retrieval on the web. Approach: It uses an incremental approach to categorize users by constructing a contextual base. The latter is composed of two types of context (static and dynamic) obtained using the users' profiles. The architecture proposed was implemented using .Net environment to perform queries reformulating tests. Results: The experiments gives at the end of this article show that the precision of the returned content is effectively improved. The tests were performed with the most popular searching engine (i.e. Google, Bind and Yahoo) selected in particular for their high selectivity. Among the given results, we found that query reformulation improve the first three results by 10.7% and 11.7% of the next seven returned elements. So as we can see the reformulation of users' initial queries improves the pertinence of returned content.Comment: 8 page