Probabilistic latent semantic analysis

About: Probabilistic latent semantic analysis is a research topic. Over the lifetime, 2884 publications have been published within this topic receiving 198341 citations. The topic is also known as: PLSA.

Sentiment Analysis of Hotel Aspect Using Probabilistic Latent Semantic Analysis, Word Embedding and LSTM

Abstract: In the industrial era 5.0, product reviews are necessary for the sustainability of a company. Product reviews are a User Generated Content (UGC) feature which describes customer satisfaction. The researcher used five hotel aspects including location, meal, service, comfort, and cleanliness to measure customer satisfaction. Each product review was preprocessed into a term list document. In this context, we proposed the Probabilistic Latent Semantic Analysis (PLSA) method to produce a hidden topic. Semantic Similarity was used to classify topics into five hotel aspects. The Term Frequency-Inverse Corpus Frequency (TF-ICF) method was used for weighting each term list, which had been expanded from each cluster in the document. The researcher used Word embedding to obtain vector values in the deep learning method from Long Short-Term Memory (LSTM) for sentiment classification. The result showed that the combination of the PLSA + TF ICF 100% + Semantic Similarity method was superior are 0.840 in the fifth categorization of the hotel aspects; the Word Embedding + LSTM method outperformed the sentiment classification at value 0.946; the service aspect received positive sentiment value higher are 45.545 than the other aspects; the comfort aspect received negative sentiment value higher are 12.871 than the other aspects. Other results also showed that sentiment was affected by the aspects.

Latent semantic indexing based on vsm

Abstract: The traditional vector space model of information retrieval technology,just statistical key words in the document frequency,search results do not reflect the relevance of the documentIn order to solve semantic keyword search when the problem of mining potential,a vector space model based on Latent Semantic Indexing improved algorithm,comparative experiments show that the algorithm can effectively improve the retrieval precision

A latent class MDS model with spatial constraints for non-stationary spatial covariance estimation

Abstract: Multidimensional scaling (MDS) has played an important role in non-stationary spatial covariance structure estimation and in analyzing the spatiotemporal processes underlying environmental studies. A combined cluster-MDS model, including geographical spatial constraints, has been previously proposed by the authors to address the estimation problem in oversampled domains in a least squares framework. In this paper is formulated a general latent class model with spatial constraints that, in a maximum likelihood framework, allows to partition the sample stations into classes and simultaneously to represent the cluster centers in a low-dimensional space, while the stations and clusters retain their spatial relationships. A model selection strategy is proposed to determine the number of latent classes and the dimensionality of the problem. Real and artificial data sets are analyzed to test the performance of the model.

LSI vs. Wordnet Ontology in Dimension Reduction for Information Retrieval

Abstract: In the area of information retrieval, the dimension of doc- ument vectors plays an important role Firstly, with higher dimensions index structures suer the "curse of dimensionality" and their eciency rapidly decreases Secondly, we may not use exact words when looking for a document, thus we miss some relevant documents LSI (Latent Se- mantic Indexing) is a numerical method, which discovers latent semantic in documents by creating concepts from existing terms However, it is hard to compute LSI In this article, we oer a replacement of LSI with a projection matrix created from WordNet hierarchy and compare it with LSI