Results 231 to 240 of about 118,657 (309)

Experimental explorations on short text topic mining between LDA and NMF based Schemes

Knowledge-Based Systems, 2019
Learning topics from short texts has become a critical and fundamental task for understanding the widely-spread streaming social messages, e.g., tweets, snippets and questions/answers.
Zhiwen Ye, Jianying Lin
exaly   +2 more sources

An association-constrained LDA model for joint extraction of product aspects and opinions

Information Sciences, 2020
The Latent Dirichlet Allocation (LDA) model, which is a document-level probabilistic model, has been widely used in topic modeling. However, an essential issue of the LDA is its shortage in identifying co-occurrence relationships (e.g., aspect-aspect ...
Keli Xiao, Xiping Liu, Dexi Liu
exaly   +2 more sources

Concept-LDA: Incorporating Babelfy into LDA for aspect extraction

Journal of Information Science, 2019
Latent Dirichlet allocation (LDA) is one of the probabilistic topic models; it discovers the latent topic structure in a document collection. The basic assumption under LDA is that documents are viewed as a probabilistic mixture of latent topics; a topic has a probability distribution over words and each document is modelled on the basis of a bag-of ...
Ekin Ekinci, Sevinç Ilhan Omurca
openaire   +3 more sources

Sparse Trace Ratio LDA for Supervised Feature Selection

IEEE Transactions on Cybernetics, 2023
Classification is a fundamental task in the field of data mining. Unfortunately, high-dimensional data often degrade the performance of classification.
Zhengxin Li, F. Nie, Danyang Wu, Z. Wang, Xuelong Li   +4 more
semanticscholar   +1 more source

Rapid classification of commercial Cheddar cheeses from different brands using PLSDA, LDA and SPA–LDA models built by hyperspectral data

Journal of Food Measurement and Characterization, 2019
Tong Lei, Da-Wen Sun, Sun Da-Wen
exaly   +2 more sources

Equivalence between LDA/QR and Direct LDA

International Journal of Cognitive Informatics and Natural Intelligence, 2011
Singularity problems of scatter matrices in Linear Discriminant Analysis (LDA) are challenging and have obtained attention during the last decade. Linear Discriminant Analysis via QR decomposition (LDA/QR) and Direct Linear Discriminant analysis (DLDA) are two popular algorithms to solve the singularity problem.
Rong-Hua Li, Shuang Liang 0001, George Baciu, Eddie C. L. Chan   +3 more
openaire   +1 more source

A new topic modeling based approach for aspect extraction in aspect based sentiment analysis: SS-LDA

Expert systems with applications, 2020
With the widespread use of social networks, blogs, forums and e-commerce web sites, the volume of user generated textual data is growing exponentially. User opinions in product reviews or in other textual data are crucial for manufacturers, retailers and
B. Özyurt, M. Ali Akcayol
semanticscholar   +1 more source

LDA-LFM

ACM SIGAPP Applied Computing Review, 2021
Most of the existing recommender systems are based only on the rating data, and they ignore other sources of information that might increase the quality of recommendations, such as textual reviews, or user and item characteristics. Moreover, the majority of those systems are applicable only on small datasets (with thousands of observations) and are ...
Tatev Karen Aslanyan, Flavius Frasincar
openaire   +1 more source

Multi-co-training for document classification using various document representations: TF-IDF, LDA, and Doc2Vec

Information Sciences, 2019
The purpose of document classification is to assign the most appropriate label to a specified document. The main challenges in document classification are insufficient label information and unstructured sparse format.
Donghwa Kim, Deokseong Seo, Suhyoun Cho, Pilsung Kang   +3 more
semanticscholar   +1 more source

Scores selection via Fisher's discriminant power in PCA-LDA to improve the classification of food data.

Food Chemistry, 2021
This paper proposes an adaptation of the Fisher's discriminability criterion (named here as discriminant power, DP) for choosing principal components (obtained from Principal Component Analysis, PCA), which will be used to construct supervised Linear ...
Valber Elias de Almeida, David Douglas de Sousa Fernandes, P. H. Diniz, Adriano de Araújo Gomes, Germano Véras, R. Galvão, Mario C U Araujo   +6 more
semanticscholar   +1 more source