Our experiments on a large Twitter dataset show that there are more meaningful and unique bursty topics in the top-ranked results returned by our model than an LDA baseline and two degenerate variations of our model.

)f tweets (or words in the case of the LDA model) 1ssigned to the topics and take the top-30 bursty top-cs from each model.

In the case of the LDA mod-:1, only 23 bursty topics were detected.

To discover topics, we can certainly apply standard topic models such as LDA (Blei et al., 2003), but with standard LDA temporal information is lost during topic discovery.

We find that compared with bursty topics discovered by standard LDA and by two degenerate variations of our model, bursty topics discovered by our model are more accurate and less redundant within the top-ranked results.

In standard LDA , a document contains a mixture of topics, represented by a topic distribution, and each word has a hidden topic label.

We also consider a standard LDA model in our experiments, where each word is associated with a hidden topic.

Just like standard LDA , our topic model itself finds a set of topics represented by gbc but does not directly generate bursty topics.

The performance of WTMF on CDR is compared with (a) an Information Retrieval model (IR) that is based on surface word matching, (b) an n-gram model (N-gram) that captures phrase overlaps by returning the number of overlapping ngrams as the similarity score of two sentences, (c) LSA that uses svds() function in Matlab, and (d) LDA that uses Gibbs Sampling for inference (Griffiths and Steyvers, 2004).

WTMF is also compared with all existing reported SS results on L106 and MSR04 data sets, as well as LDA that is trained on the same data as WTMF.

To eliminate randomness in statistical models (WTMF and LDA ), all the reported results are averaged over 10 runs.

Latent variable models, such as Latent Semantic Analysis [LSA] (Landauer et al., 1998), Probabilistic Latent Semantic Analysis [PLSA] (Hofmann, 1999), Latent Dirichlet Allocation [ LDA ] (Blei et al., 2003) can solve the two issues naturally by modeling the semantics of words and sentences simultaneously in the low-dimensional latent space.

Therefore, PLSA finds a topic distribution for each concept definition that maximizes the log likelihood of the corpus X ( LDA has a similar form):

Both Latent Dirichlet Allocation ( LDA ) (Blei et al., 2003) and Probabilistic Latent Semantic Analysis (PLSA) (Hofmann, 1999) are types of topic models.

LDA is the most common topic model currently in use, therefore we exploit it for mining topics in this paper.

Here, we first give a brief description of LDA .

Unlike document-topic distribution that can be directly learned by LDA tools, we need to estimate the rule-topic distribution according to our requirement.

bution of every documents inferred by LDA tool.

The topic assignments are output by LDA tool.

The k-th dimension P(z = k3|d) means the probability of topic k: given document d. Different from rule-topic distribution, the document-topic distribution can be directly inferred by an off-the-shelf LDA tool.

Latent variable models, such as latent Dirichlet allocation ( LDA ) (Blei et al., 2003) and probabilistic latent semantic analysis (PLSA) (Hofmann, 1999), have been used in the past to facilitate social science research.

SAGE (Eisenstein et al., 2011a), a recently proposed sparse additive generative model of language, addresses many of the drawbacks of LDA .

Another advantage, from a social science perspective, is that SAGE can be derived from a standard logit random-utility model of judicial opinion writing, in contrast to LDA .

Related research efforts include using the LDA model for topic modeling in historical newspapers (Yang et al., 2011), a rule-based approach to extract verbs in historical Swedish texts (Pettersson and Nivre, 2011), a system for semantic tagging of historical Dutch archives (Cybulska and Vossen, 2011).

(2010) study the effect of the context of interaction in blogs using a standard LDA model.

To address the over-parameterization, lack of expressiveness and robustness issues in LDA , the SAGE (Eisenstein et al., 2011a) framework draws a

In this SME model, we still have the same Dirichlet a, the latent topic proportion 6, and the latent topic variable 2 as the original LDA model.

In contrast to traditional multinomial distribution of words in LDA models, we approximate the conditional word distribution in the document d as the

To this end we interpret the de-scriptors as words in documents, and train a standard LDA model based on these documents.

We also train a standard LDA model to obtain the theme of a sentence.

The LDA model assigns each word to a topic.

In our experiments, we use the meta-descriptors of a document as side information and train a standard LDA model to find the theme of a document.

This model is a minor variation on standard LDA and the difference is that instead of drawing an observation from a hidden topic variable, we draw multiple observations from a hidden topic variable.

Topic modeling was performed with Mallet (Mccallum, 2002), a standard implementation of LDA , using a Chinese sto-plist and setting the per-document Dirichlet parameter a = 0.01.

, K} over each document, using Latent Dirichlet Allocation ( LDA ) (Blei et al., 2003).

For this case, we also propose a local LDA model (LTM), which treats each sentence as a separate document.

DF-LDA adds constraints to LDA .

The standard LDA and existing aspect and sentiment models (ASMs) are mostly governed by the phenomenon called “higher-order co-occurrence” (Heinrich, 2009), i.e., based on how often terms co-occur in different contexts].

Existing works are based on two basic models, pLSA (Hofmann, 1999) and LDA (Blei et al., 2003).