Discovering Data and Information Quality Research Insights Gained through Latent Semantic Analysis

2012 ◽  
Vol 3 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Roger Blake ◽  
Ganesan Shankaranarayanan
Keyword(s):  
Business Intelligence ◽  
Latent Semantic Analysis ◽  
Information Quality ◽  
Semantic Analysis ◽  
Recent Decade ◽  
Research Area ◽  
Quality Research ◽  
The Core ◽  
Data Querying ◽  
Do So

In the recent decade, the field of data and information quality (DQ) has grown into a research area that spans multiple disciplines. The motivation here is to help understand the core topics and themes that constitute this area and to determine how those topics and themes from DQ relate to business intelligence (BI). To do so, the authors present the results of a study which mines the abstracts of articles in DQ published over the last decade. Using Latent Semantic Analysis (LSA) six core themes of DQ research are identified, as well as twelve dominant topics comprising them. Five of these topics--decision support, database design and data mining, data querying and cleansing, data integration, and DQ for analytics--all relate to BI, emphasizing the importance of research that combines DQ with BI. The DQ topics from these results are profiled with BI, and used to suggest several opportunities for researchers.

Sentiment Analysis

2017 ◽  
pp. 162-174 ◽  
Author(s):  
A. M. Abirami ◽  
A. Sheik Abdullah ◽  
A. Askarunisa ◽  
S. Selvakumar ◽  
C. Mahalakshmi
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Information Extraction ◽  
Sentiment Analysis ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
User Generated Content ◽  
Aspect Extraction ◽  
The Core ◽  
Analysis Technique

It requires sophisticated streaming of big data processing to process the billions of daily social conversations across millions of sources. Dataset needs information extraction from them and it requires contextual semantic sentiment modeling to capture the intelligence through the complexity of online social discussions. Sentiment analysis is one of the techniques to capture the intelligence from Social Networks based on the user generated content. There are more and more researches evolving about sentiment classification. Aspect extraction is the core task involved in aspect based sentiment analysis. The proposed modeling uses Latent Semantic Analysis technique for aspect extraction and evaluates senti-scores of various products under study.

Task Estimation Using Latent Semantic Analysis of Visual Scenes and Spoken Words

2012 ◽  
Vol 132 (9) ◽  
pp. 1473-1480
Author(s):  
Masashi Kimura ◽  
Shinta Sawada ◽  
Yurie Iribe ◽  
Kouichi Katsurada ◽  
Tsuneo Nitta
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Spoken Words ◽  
Visual Scenes

Similarity Detection Using Latent Semantic Analysis Algorithm

2018 ◽  
Vol 6 (8) ◽  
pp. 102
Author(s):  
Priyanka R. Patil ◽  
Shital A. Patil
Keyword(s):  
Latent Semantic Analysis ◽  
Latent Dirichlet Allocation ◽  
Semantic Analysis ◽  
Mining Method ◽  
Research Papers ◽  
Information Measures ◽  
Automated Software ◽  
Day By Day ◽  
Ways Of Life ◽  
Dirichlet Allocation

Similarity View is an application for visually comparing and exploring multiple models of text and collection of document. Friendbook finds ways of life of clients from client driven sensor information, measures the closeness of ways of life amongst clients, and prescribes companions to clients if their ways of life have high likeness. Roused by demonstrate a clients day by day life as life records, from their ways of life are separated by utilizing the Latent Dirichlet Allocation Algorithm. Manual techniques can't be utilized for checking research papers, as the doled out commentator may have lacking learning in the exploration disciplines. For different subjective views, causing possible misinterpretations. An urgent need for an effective and feasible approach to check the submitted research papers with support of automated software. A method like text mining method come to solve the problem of automatically checking the research papers semantically. The proposed method to finding the proper similarity of text from the collection of documents by using Latent Dirichlet Allocation (LDA) algorithm and Latent Semantic Analysis (LSA) with synonym algorithm which is used to find synonyms of text index wise by using the English wordnet dictionary, another algorithm is LSA without synonym used to find the similarity of text based on index. LSA with synonym rate of accuracy is greater when the synonym are consider for matching.

LATENT-SEMANTIC ANALYSIS, SOCIAL NETWORKS AND NON-STRUCTURED DATA: INTERACTION METHOD

2019 ◽  
Keyword(s):  
Social Networks ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Statistical Processing ◽  
Unstructured Data ◽  
Average Score ◽  
Text Type ◽  
Internet Users ◽  
The Matrix ◽  
Human Thinking

This article examines the method of latent-semantic analysis, its advantages, disadvantages, and the possibility of further transformation for use in arrays of unstructured data, which make up most of the information that Internet users deal with. To extract context-dependent word meanings through the statistical processing of large sets of textual data, an LSA method is used, based on operations with numeric matrices of the word-text type, the rows of which correspond to words, and the columns of text units to texts. The integration of words into themes and the representation of text units in the theme space is accomplished by applying one of the matrix expansions to the matrix data: singular decomposition or factorization of nonnegative matrices. The results of LSA studies have shown that the content of the similarity of words and text is obtained in such a way that the results obtained closely coincide with human thinking. Based on the methods described above, the author has developed and proposed a new way of finding semantic links between unstructured data, namely, information on social networks. The method is based on latent-semantic and frequency analyzes and involves processing the search result received, splitting each remaining text (post) into separate words, each of which takes the round in n words right and left, counting the number of occurrences of each term, working with a pre-created semantic resource (dictionary, ontology, RDF schema, ...). The developed method and algorithm have been tested on six well-known social networks, the interaction of which occurs through the ARI of the respective social networks. The average score for author's results exceeded that of their own social network search. The results obtained in the course of this dissertation can be used in the development of recommendation, search and other systems related to the search, rubrication and filtering of information.

Web clustering based on hybrid probabilistic latent semantic analysis model

2013 ◽  
Vol 32 (11) ◽  
pp. 3018-3022 ◽  
Author(s):  
Zhi-he WANG ◽  
Ling-yun WANG ◽  
Hui DANG ◽  
Li-na PAN
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Probabilistic Latent Semantic Analysis ◽  
Analysis Model ◽  
Web Clustering
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