Deep finesse network model with multichannel syntactic and contextual features for target-specific sentiment classification

2021 ◽  
Author(s):  
Deepak Chowdary Edara ◽  
Venkatramaphanikumar Sistla ◽  
Venkata Krishna Kishore Kolli
Keyword(s):  
Network Model ◽  
Sentiment Classification ◽  
Contextual Features

scholarly journals Replicate, Walk, and Stop on Syntax: An Effective Neural Network Model for Aspect-Level Sentiment Classification

2020 ◽  
Vol 34 (05) ◽  
pp. 9685-9692
Author(s):  
Yaowei Zheng ◽  
Richong Zhang ◽  
Samuel Mensah ◽  
Yongyi Mao
Keyword(s):  
Neural Network ◽  
Random Walk ◽  
Network Model ◽  
Neural Network Model ◽  
Syntactic Structure ◽  
Contextual Information ◽  
Empirical Studies ◽  
Specific Aspect ◽  
Sentiment Classification ◽  
Benchmark Datasets

Aspect-level sentiment classification (ALSC) aims at predicting the sentiment polarity of a specific aspect term occurring in a sentence. This task requires learning a representation by aggregating the relevant contextual features concerning the aspect term. Existing methods cannot sufficiently leverage the syntactic structure of the sentence, and hence are difficult to distinguish different sentiments for multiple aspects in a sentence. We perceive the limitations of the previous methods and propose a hypothesis about finding crucial contextual information with the help of syntactic structure. For this purpose, we present a neural network model named RepWalk which performs a replicated random walk on a syntax graph, to effectively focus on the informative contextual words. Empirical studies show that our model outperforms recent models on most of the benchmark datasets for the ALSC task. The results suggest that our method for incorporating syntactic structure enriches the representation for the classification.

scholarly journals Exploring Brazilian Cultural Identity Through Reading Preferences

2021 ◽  
Author(s):  
Mariana O. Silva ◽  
Clarisse Scofield ◽  
Gabriel P. Oliveira ◽  
Danilo B. Seufitelli ◽  
Mirella M. Moro
Keyword(s):  
Cultural Identity ◽  
Network Model ◽  
Current Knowledge ◽  
Book Industry ◽  
Social Indicators ◽  
Demographic Factors ◽  
Comparison Analysis ◽  
Contextual Features ◽  
Writing Genres ◽  
Socioeconomic And Demographic Factors

In Brazil, each region has its own cultural identity regarding accent, gastronomy, customs, all of which may reflect in its literature. Specially, we believe that country's background and contextual features are directly related to what people read. Hence, we perform a cross-state comparison analysis based on Brazilian reading preferences through a multipartite network model. Also, we explore the effects of socioeconomic and demographic factors on favorite books and writing genres. Such cross-state analyses highlight how the country is culturally rich, where each region has its own distinctive culture. Our findings offer great opportunities for the Brazilian book industry by enhancing current knowledge on social indicators related to reading preferences.

scholarly journals TransCapsule Model for Sentiment Classification

2020 ◽  
Vol 2 (3) ◽  
pp. 163-169
Author(s):  
Dr. Akey Sungheetha ◽  
Dr. Rajesh Sharma R,
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Network Model ◽  
Online Reviews ◽  
Point Of View ◽  
Sentiment Classification ◽  
The Other ◽  
Learning Framework ◽  
Very High ◽  
Document Level

Aspect-level sentiment classification is the aspect of determining the text in a given document and classifying it according to the sentiment polarity with respect to the objective. However, since annotation cost is very high, it might serve a big obstacle for this purpose. However, from a consumer point of view, this is highly effective in reading document-level labelled data such as reviews which are present online using neural network. The online reviews are packed with sentiment encoded text which can be analyzed using this proposed methodology. In this paper a Transfer Capsule Network model is used which has the ability to transfer the knowledge gained at document-level to the aspect-level to classify according to the sentiment detected in the text. As the first step, the sentence is broken down in semantic representations using aspect routing to form semantic capsule data of both document-level and aspect-level. This routing approach is extended to group the semantic capsules for transfer learning framework. The effectiveness of the proposed methodology are experimented and demonstrated to determine how superior they are to the other methodologies proposed.

A Network Model for Optimizing two Echelon Multi-Item Repair Level Decisions

1991 ◽  
Vol 8 (1) ◽  
pp. 77-90
Author(s):  
W. Steven Demmy ◽  
Lawrence Briskin
Keyword(s):  
Network Model
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