You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Recently by the development of the Internet and the Web, different types of social media such as web blogs become an immense source of text data. Through the processing of these data, it is possible to discover practical information about different topics, individual’s opinions and a thorough understanding of the society. Therefore, applying models which can automatically extract the subjective information from documents would be efficient and helpful. Topic modeling methods and sentiment analysis are the raised topics in natural language processing and text mining fields. In this paper a new structure for joint sentiment-topic modeling based on a Restricted Boltzmann Machine (RBM) which is a type of neural networks is proposed. By modifying the structure of RBM as well as appending a layer which is analogous to sentiment of text data to it, we propose a generative structure for joint sentiment topic modeling based on neural networks. The proposed method is supervised and trained by the Contrastive Divergence algorithm. The new attached layer in the proposed model is a layer with the multinomial probability distribution which can be used in text data sentiment classification or any other supervised application. The proposed model is compared with existing models in the experiments such as evaluating as a generative model, sentiment classification, information retrieval and the corresponding results demonstrate the efficiency of the method.

Cited Abstract(s):
[1]: It is possible to combine multiple latent-variable models of the same data by multiplying their probability distributions together and then renormalizing. This way of combining individual "expert" models makes it hard to generate samples from the combined model but easy to infer the values of the latent variables of each expert, because the combination rule ensures that the latent variables of different experts are conditionally independent when given the data. A product of experts (PoE) is therefore an interesting candidate for a perceptual system in which rapid inference is vital and generation is unnecessary. Training a PoE by maximizing the likelihood of the data is difficult because it is hard even to approximate the derivatives of the renormalization term in the combination rule. Fortunately, a PoE can be trained using a different objective function called "contrastive divergence" whose derivatives with regard to the parameters can be approximated accurately and efficiently. Examples are presented of contrastive divergence learning using several types of expert on several types of data.

[2]: 

[3]: In this paper, we present a simple and effective topic correlation model (TCM) for cross-modal multimedia retrieval by jointly modeling the text and image components in multimedia documents. In this model, the image component is represented by the bag-of-features model based on local scale-invariant feature transform features, meanwhile the text component is described by a topic distribution learned from a latent topic model. Statistical correlations between these two mid-level features are investigated by mapping them into a semantic space. These cross-modality correlations are used to calculate the conditional probabilities of answers in one modality while given query in the other modality. The model is tested on three cross-modal retrieval benchmark problems including Wikipedia documents in both English and Chinese. Experimental results have demonstrated that the new TCM model achieves the best performance compared to recent state-of-the-art cross-modal retrieval models on the given benchmarks.

[4]: We describe a new model for learning meaningful representations of text documents from an unlabeled collection of documents. This model is inspired by the recently proposed Replicated Softmax, an undirected graphical model of word counts that was shown to learn a better generative model and more meaningful document representations. Specifically, we take inspiration from the conditional mean-field recursive equations of the Replicated Softmax in order to define a neural network architecture that estimates the probability of observing a new word in a given document given the previously observed words. This paradigm also allows us to replace the expensive softmax distribution over words with a hierarchical distribution over paths in a binary tree of words. The end result is a model whose training complexity scales logarithmically with the vocabulary size instead of linearly as in the Replicated Softmax. Our experiments show that our model is competitive both as a generative model of documents and as a document representation learning algorithm.

[5]: We describe a new approach for modeling the distribution of high-dimensional vectors of discrete variables. This model is inspired by the restricted Boltzmann machine (RBM), which has been shown to be a powerful model of such distributions. However, an RBM typically does not provide a tractable distribution estimator, since evaluating the probability it assigns to some given observation requires the computation of the so-called partition function, which itself is intractable for RBMs of even moderate size. Our model circumvents this diculty by decomposing the joint distribution of observations into tractable conditional distributions and modeling each conditional using a non-linear function similar to a conditional of an RBM. Our model can also be interpreted as an autoencoder wired such that its output can be used to assign valid probabilities to observations. We show that this new model outperforms other multivariate binary distribution estimators on several datasets and performs similarly to a large (but intractable) RBM.
The Restricted Boltzmann Machine (RBM) is an unsupervised two-layer neural network for the estimation of distribution of binary input data. This generative probabilistic model was first introduced in 1986 by Smolensky [2] and was later developed by Hinton in 2002 [1] . Inspired by the RBM model, in 2011 introduced the Neural Autoregressive Distribution Estimation (NADE) [5] , which is an unsupervised generative probabilistic method for modeling the probability of discrete data. NADE eliminated the limitation of RBM in high dimensional joint probability estimations by the use of fully visible Bayes networks for probability calculations. The earliest neural network-based topic model is Replicated Softmax Model (RS) introduced by Hinton and Salakhutdinov in 2009 [3] , which is an extension of the RBM model used to detect the distribution of topics in text data [3] . In 2012, Larochelle and Lauly combined NADE and RS to develop an unsupervised neural network-based topic modeling method called the Document Neural Autoregressive Distribution Estimation (DocNADE) [4] .