A Hybrid Approach of Text Summarization Using Latent Semantic Analysis and Deep Learning

2018 ◽  
Author(s):  
Chintan Shah ◽  
Anjali Jivani
Keyword(s):  
Deep Learning ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Hybrid Approach ◽  
Text Summarization

RDRLLJ: Integrating Deep Learning Approach with Latent Semantic Analysis for Document Retrieval

2021 ◽  
pp. 999-1007
Author(s):  
Saicharan Gadamshetti ◽  
Gerard Deepak ◽  
A. Santhanavijayan ◽  
K. R. Venugopal
Keyword(s):  
Deep Learning ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Document Retrieval ◽  
Learning Approach

A Formal Technique for Text Summarization from Web Pages by using Latent Semantic Analysis

2019 ◽  
Vol 148 (3) ◽  
pp. 11-22
Author(s):  
J. Guadalupe Ramos ◽  
Isela Navarro-Alatorre ◽  
Georgina Flores Becerra ◽  
Omar Flores-Sánchez
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Text Summarization ◽  
Web Pages

Text summarization using a trainable summarizer and latent semantic analysis

2005 ◽  
Vol 41 (1) ◽  
pp. 75-95 ◽  
Author(s):  
Jen-Yuan Yeh ◽  
Hao-Ren Ke ◽  
Wei-Pang Yang ◽  
I-Heng Meng
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Text Summarization

scholarly journals Sentiment Analysis in Twitter Based on Knowledge Graph and Deep Learning Classification

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2739
Author(s):  
Fernando Andres Lovera ◽  
Yudith Coromoto Cardinale ◽  
Masun Nabhan Homsi
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Semantic Analysis ◽  
Hybrid Approach ◽  
Black Box ◽  
Machine Learning Techniques ◽  
Graph Similarity ◽  
Learning Techniques ◽  
Knowledge Graphs ◽  
N Gram

The traditional way to address the problem of sentiment classification is based on machine learning techniques; however, these models are not able to grasp all the richness of the text that comes from different social media, personal web pages, blogs, etc., ignoring the semantic of the text. Knowledge graphs give a way to extract structured knowledge from images and texts in order to facilitate their semantic analysis. This work proposes a new hybrid approach for Sentiment Analysis based on Knowledge Graphs and Deep Learning techniques to identify the sentiment polarity (positive or negative) in short documents, such as posts on Twitter. In this proposal, tweets are represented as graphs; then, graph similarity metrics and a Deep Learning classification algorithm are applied to produce sentiment predictions. This approach facilitates the traceability and interpretability of the classification results, thanks to the integration of the Local Interpretable Model-agnostic Explanations (LIME) model at the end of the pipeline. LIME allows raising trust in predictive models, since the model is not a black box anymore. Uncovering the black box allows understanding and interpreting how the network could distinguish between sentiment polarities. Each phase of the proposed approach conformed by pre-processing, graph construction, dimensionality reduction, graph similarity, sentiment prediction, and interpretability steps is described. The proposal is compared with character n-gram embeddings-based Deep Learning models to perform Sentiment Analysis. Results show that the proposal is able to outperforms classical n-gram models, with a recall up to 89% and F1-score of 88%.

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