Temporal Topic Analysis with Endogenous and Exogenous Processes

We consider the problem of modeling temporal textual data taking endogenous and exogenous processes into account. Such text documents arise in real world applications, including job advertisements and economic news articles, which are influenced by the fluctuations of the general economy. We propose a hierarchical Bayesian topic model which imposes a "group-correlated" hierarchical structure on the evolution of topics over time incorporating both processes, and show that this model can be estimated from Markov chain Monte Carlo sampling methods. We further demonstrate that this model captures the intrinsic relationships between the topic distribution and the time-dependent factors, and compare its performance with latent Dirichlet allocation (LDA) and two other related models. The model is applied to two collections of documents to illustrate its empirical performance: online job advertisements from DirectEmployers Association and journalists' postings on BusinessInsider.com

Automatic document clustering using topic analysis

Web users are demanding more out of current search engines. This can be noticed by the behaviour of users when interacting with search engines [12, 28]. Besides traditional query/results interactions, other tools are springing up on the web. An example of such tools includes web document clustering systems. The idea is for the user to interact with the system by navigating through an organised hierarchy of topics. Document clustering is ideal for unspecified search goals or for the exploration of a topic by the inexpert [21]. Document clustering is there to transform the current interactions of searching through a large amount of links into an efficient interaction where the interaction is navigation through hierarchies. This report will give an overview of the major work in this area, we will also propose our current work, progress and pitfalls which are being tackled.peer-reviewe

Sentiment and topic analysis of dialogue transcripts

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 52-53).The field of natural language processing has had success in analyzing sentiment and topics on written text, but similar analysis on dialogue is more difficult due to the fragmented and informal nature of speech. This work explores sentiment and topic analysis on data from the Switchboard dialogue corpus, as well as a dataset of recorded dialogues between parents and children while reading an interactive e-book. The goal was to be able to identify the emotion and mood of the dialogue in order to make inferences about what parents and children generally talk about when reading the book because conversations between an adult and child while reading a book can greatly contribute to the learning and development of young children.by Anjali Muralidhar.M. Eng

SITUATION AWARE TOPIC ANALYSIS FOR KEYNOTE/SPEAKER EVENTS

Techniques are described herein to provide speaker insights for a talk by correlating audience mood and engagement data (derived from video feed) with speech data. With this framework a speaker obtains a summary of topics that were relevant to the crowd. This technique helps a speaker to design future content based on the metrics and topics obtained using this framework. This solution can bring a lot of value to people who intend to design content for keynotes that will be relevant to the crowd

Orthonormal Explicit Topic Analysis for Cross-lingual Document Matching

McCrae J, Cimiano P, Klinger R. Orthonormal Explicit Topic Analysis for Cross-lingual Document Matching. In: Proceedings of the 2013 Conference on Empirical Natural Language Processing. 2013: 1732-1740

User Needs Mining Based on Topic Analysis of Online Reviews

The purpose of this paper is to aggregate the topic information of online review text and clarify the user needs. We conducted the study on online reviews of women’s clothing store of Taobao.com with semantic analysis and text mining. Online reviews were collected by means of web crawler. Using Chinese word segmentation tool and data analysis tool, the word frequency statistics was realized. The statistical software was used for the clustering analysis and multidimensional scaling analysis of high frequency keywords. The results show that the content of online reviews mainly includes four topics: basic features of products, additional features of products, user experience and product display. It reveals the potential user needs of women’s clothing store of Taobao.com, which cannot only help consumers to make rational decisions, but also provide guidance to merchants and manufacturers

Near Real-Time Sentiment and Topic Analysis of Sport Events

Sport events’ media consumption patterns have started transitioning to a multi-screen paradigm, where, through multitasking, viewers are able to search for additional information about the event they are watching live, as well as contribute with their perspective of the event to other viewers. The audiovisual and multimedia industries, however, are failing to capitalize on this by not providing the sports’ teams and those in charge of the audiovisual production with insights on the final consumers perspective of sport events. As a result of this opportunity, this document focuses on presenting the development of a near real-time sentiment analysis tool and a near real-time topic analysis tool for the analysis of sports events’ related social media content that was published during the transmission of the respective events, thus enabling, in near real-time, the understanding of the sentiment of the viewers and the topics being discussed through each event.Os padrões de consumo de media, têm vindo a mudar para um paradigma de ecrãs múltiplos, onde, através de multitasking, os telespetadores podem pesquisar informações adicionais sobre o evento que estão a assistir, bem como partilhar a sua perspetiva do evento. As indústrias do setor audiovisual e multimédia, no entanto, não estão a aproveitar esta oportunidade, falhando em fornecer às equipas desportivas e aos responsáveis pela produção audiovisual uma visão sobre a perspetiva dos consumidores finais dos eventos desportivos. Como resultado desta oportunidade, este documento foca-se em apresentar o desenvolvimento de uma ferramenta de análise de sentimento e uma ferramenta de análise de tópicos para a análise, em perto de tempo real, de conteúdo das redes sociais relacionado com eventos esportivos e publicado durante a transmissão dos respetivos eventos, permitindo assim, em perto de tempo real, perceber o sentimento dos espectadores e os tópicos mais falados durante cada evento