Mining Interpretable Rules for Sentiment and Semantic Relation Analysis Using Tsetlin Machines

2020 ◽  
pp. 67-78
Author(s):  
Rupsa Saha ◽  
Ole-Christoffer Granmo ◽  
Morten Goodwin
Keyword(s):  
Semantic Relation ◽  
Relation Analysis

Towards a Sentiment Analysis Model Based on Semantic Relation Analysis

2018 ◽  
Vol 9 (2) ◽  
pp. 54-75 ◽  
Author(s):  
Thien Khai Tran ◽  
Tuoi Thi Phan
Keyword(s):  
Sentiment Analysis ◽  
Semantic Relation ◽  
Effective Model ◽  
Analysis Model ◽  
Model Based ◽  
Relation Analysis ◽  
Novel Approaches ◽  
Syntactic Dependency

Sentiment analysis is an important new field of research that has attracted the attention not only of researchers, but also businesses and organizations. In this article, the authors propose an effective model for aspect-based sentiment analysis for Vietnamese. First, sentiment dictionaries and syntactic dependency rules were combined to extract reliable word pairs (sentiment - aspect). They then relied on ontology to group these aspects and determine the sentiment polarity of each. They introduce two novel approaches in this work: 1) in order to “smooth” the sentiment scaling (rather than using discrete categories of 1, 0, and -1) for fined-grained classification, then extract multi-word sentiment phrases instead of sentiment words, and 2) the focus is not only on adjectives but also nouns and verbs. Initial evaluations of the system using real reviews show promising results.

A link-based approach to semantic relation analysis

2015 ◽  
Vol 154 ◽  
pp. 127-138 ◽  
Author(s):  
Xin Cheng ◽  
Duoqian Miao ◽  
Can Wang
Keyword(s):  
Semantic Relation ◽  
Relation Analysis

scholarly journals Towards a sentiment analysis model based on semantic relation analysis

2018 ◽  
Vol 20 (K7) ◽  
pp. 21-27
Author(s):  
Thien Khai Tran ◽  
Tuoi Thi Phan
Keyword(s):  
Sentiment Analysis ◽  
Semantic Relation ◽  
Effective Model ◽  
Analysis Model ◽  
Model Based ◽  
Sentiment Dictionary ◽  
Relation Analysis ◽  
Syntactic Dependency ◽  
Positive Results

In this paper, we propose an effective model for aspect-based sentiment analysis. First, we combined a sentiment dictionary and syntactic dependency rules to extract reliable word pairs (sentiment — aspect). Then, thanks to ontology, we grouped those aspects and determined the sentiment polarity of each. When we conducted experiments on real reviews, the system showed positive results.

scholarly journals LOGICO SEMANTIC RELATION ANALYSIS OF CLAUSE COMPLEX IN CNN NEWS

2020 ◽  
Vol 1 (1) ◽  
pp. 24
Author(s):  
Niken Sri Noviandari ◽  
Dolar Yuwono
Keyword(s):  
Semantic Relation ◽  
Qualitative Approach ◽  
Data Display ◽  
Dominant Type ◽  
The Third ◽  
Data Source ◽  
Analysis Design ◽  
Relation Analysis ◽  
Clause Complex ◽  
Logical Semantic

This study deals with Logico Semantic Relation in CNN News text. The objectives of this study is to discover the types of Logico Semantic Relation of Clause Complex Used in CNN News and to know the dominant type  of logical semantic systems interpreted in CNN News.  The researcher applied qualitative approach and used content analysis design.  The technique of collecting data was documentation. The data of the research was Logico Semantic Relation meanwhile the news text of CNN was as the data source of the research.  The data were analyzed by data reduction, data display, and conclusion drawing/verification. The findings showed that (1) The types of Logico Semantic Relation used in the five news texts of CNN were Expansion (Elaboration, Enhancement, and Extension) and Projection (Locution). The total number of Logico Semantic Relation was 201 or 100% which consisted of 153 items or 76,10% of expansion and 48 items or 23,90% of projection.  (2)  Expansion (Elaboration) was the most dominant type among all kinds of Logico Semantic Relation which appeared in 92 times or 45,80%. The second rank was projection (locution) that was 48 times or 23,90%. The third position was expansion (Enhancement) which occurred 33 times or 16,40%. Meanwhile, Expansion (Extension) appeared 28 times or 13,90%, and the last one was projection (idea), which had no percentage (0 times or 0.00%). The researcher concludes that there are two types of Logico Semantic Relation used in the CNN news text, those are Expansion (Elaboration, Enhancement, and Extension) and Projection (Locution). The most dominant type of Logico Semantic Relation that appears in the text is Expansion (Elaboration). 

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