Intelligent Disaster Response via Social Media Analysis A Survey <s> Topic Discovery and Evolution <s> We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model. <s> BIB001 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Topic Discovery and Evolution <s> Discovering and tracking topic shifts in news constitutes a new challenge for applications nowadays. Topics evolve,emerge and fade, making it more difficult for the journalist -or the press consumer- to decrypt the news. For instance, the current Syrian chemical crisis has been the starting point of the UN Russian initiative and also the revival of the US France alliance. A topical mapping representing how the topics evolve in time would be helpful to contextualize information. As far as we know, few topic tracking systems can provide such temporal topic connections. In this paper, we introduce a novel framework inspired from Collective Factorization for online topic discovery able to connect topics between different time-slots. The framework learns jointly the topics evolution and their time dependencies. It offers the user the ability to control, through one unique hyper-parameter, the tradeoff between the past accumulated knowledge and the current observed data. We show, on semi-synthetic datasets and on Yahoo News articles, that our method is competitive with state-of-the-art techniques while providing a simple way to monitor topics evolution (including emerging and disappearing topics). <s> BIB002 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Topic Discovery and Evolution <s> Topic discovery and evolution (TDE) has been a problem which has gained long standing interest in the research community. The goal in topic discovery is to identify groups of keywords from large corpora so that the information in those corpora are summarized succinctly. The nature of text corpora has changed dramatically in the past few years with the advent of social media. Social media services allow users to constantly share, follow and comment on posts from other users. Hence, such services have given a new dimension to the traditional text corpus. The new dimension being that today's corpora have a social context embedded in them in terms of the community of users interested in a particular post, their profiles etc. We wish to harness this social context that comes along with the textual content for TDE. In particular, our goal is to both qualitatively and quantitatively analyze when social context actually helps with TDE. Methodologically, we approach the problem of TDE by a proposing non-negative matrix factorization (NMF) based model that incorporates both the textual information and social context information. We perform experiments on large scale real world dataset of news articles, and use Twitter as the platform providing information about the social context of these news articles. We compare with and outperform several state-of-the-art baselines. Our conclusion is that using the social context information is most useful when faced with topics that are particularly difficult to detect. <s> BIB003
Several methods are used for discovering topics from a corpus of text (news articles, tweets, Facebook posts, etc): Latent Dirichlet Allocation (LDA), Non-negative Matrix Factorization (NMF), Term frequency-inverse document frequency (tf-idf), and PLSI. LDA BIB001 considers a fixed number of latent topics for the corpus and finds the probability of each document belonging to each topic. A topic itself is a distribution over words (vocabulary). In NMF  , the document-word matrix would be factorized into two matrices, document-topic and topic-word. NMF describes both documents and terms in the environment of latent topics. tfidf is widely used in Information Retrieval. Using tf-idf, each document is represented using a vector of size V (vocabulary size). Element ij of this the tf-idf vector is the frequency of word i in document j time the inverse of the total frequency of word i in the whole corpus of documents. Probabilistic latent semantic indexing (PLSI) is based on the assumption that each document has one topic and words and documents are conditionally independent given the topic of the document. Hence the probability of a word wn in document d is calculated using p(wn, d) = p(d) z p(wn|z)p(z|d). As the disaster proceeds in its stages, concerns of the affected people evolve and hence the topics of discussion. There is a body of research modeling evolving and fading topics which are usually known as topic discovery and evolution (TDE) methods. Vaca et al. BIB002 and Kalyanam et al.  in separate works, similarly model this problem using a modified version of NMF. In these proposed meth-ods, besides decomposing the document-word matrix into document-topic and topic-word matrices, a matrix M would be considered which models how topics evolve from each time step to the next. Hence one topic matrix is calculated at each point of time. The entropy of topics in each time step indicates its status, continuing, evolving, or new. In another work BIB003 , besides using the evolution of topics, they have also exploited social context. Their method is based on the assumption that members of one community have similar interest in the topics and show that this information improves topic discovery results especially when the topic has a large set of keywords associated with it or evolves much over time and hence is difficult to detect. However, the persistence of the user who shows interest in these topics help the detection methods.