scholarly journals Sentiment Analysis Based on Psychological and Linguistic Features for Spanish Language

2017 ◽  
pp. 73-92 ◽  
Author(s):  
María Pilar Salas-Zárate ◽  
Mario Andrés Paredes-Valverde ◽  
Miguel Ángel Rodríguez-García ◽  
Rafael Valencia-García ◽  
Giner Alor-Hernández
Keyword(s):  
Sentiment Analysis ◽  
Spanish Language ◽  
Linguistic Features

Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis

2017 ◽  
Vol 44 (2) ◽  
pp. 184-202 ◽  
Author(s):  
Adel Assiri ◽  
Ahmed Emam ◽  
Hmood Al-Dossari
Keyword(s):  
Sentiment Analysis ◽  
Large Scale ◽  
Training Data ◽  
Linguistic Features ◽  
Algorithm Development ◽  
Domain Independence ◽  
Low Performance

Sentiment analysis (SA) techniques are applied to assess aspects of language that are used to express feelings, evaluations and opinions in areas such as customer sentiment extraction. Most studies have focused on SA techniques for widely used languages such as English, but less attention has been paid to Arabic, particularly the Saudi dialect. Most Arabic SA studies have built systems using supervised approaches that are domain dependent; hence, they achieve low performance when applied to a new domain different from the learning domain, and they require manually labelled training data, which are usually difficult to obtain. In this article, we propose a novel lexicon-based algorithm for Saudi dialect SA that features domain independence. We created an annotated Saudi dialect dataset and built a large-scale lexicon for the Saudi dialect. Then, we developed our weighted lexicon-based algorithm. The proposed algorithm mines the associations between polarity and non-polarity words for the dataset and then weights these words based on their associations. During algorithm development, we also proposed novel rules for handling some linguistic features such as negation and supplication. Several experiments were performed to evaluate the performance of the proposed algorithm.

scholarly journals AspectSA: Unsupervised system for aspect based sentiment analysis in Spanish

Prospectiva ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 87-95
Author(s):  
Carlos Henriquez Miranda ◽  
Edgardo Buelvas
Keyword(s):  
Sentiment Analysis ◽  
Spanish Language ◽  
Specific Domain ◽  
Fine Grain ◽  
Great Ease ◽  
Unsupervised Approach

This paper describes an unsupervised system for sentiment analysis is presented in Spanish. The system performs a complete fine grain analysis, where the most important characteristics or aspects of an opinion are identified in order to determine their sentiment associated. The unsupervised approach used allows to extract, identify and sentiment classify, from the analysis of opinions in Spanish in a specific domain, allowing to scale to another language and domain with great ease. For the validation of AspectSA, several experiments were carried out using corpus of opinions in the restaurant domain. The results obtained exceeded the majority of existing systems for the Spanish language.

Systematic literature review of sentiment analysis in the Spanish language

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sonia Osorio Angel ◽  
Adriana Peña Pérez Negrón ◽  
Aurora Espinoza-Valdez
Keyword(s):  
Literature Review ◽  
Sentiment Analysis ◽  
Systematic Literature Review ◽  
Design Methodology ◽  
Spanish Language ◽  
Point Of View ◽  
Classification Systems ◽  
Content Type ◽  
Linguistic Resources ◽  
Hybrid Approaches

PurposeMost studies on Sentiment Analysis are performed in English. However, as the third most spoken language on the Internet, Sentiment Analysis for Spanish presents its challenges from a semantic and syntactic point of view. This review presents a scope of the recent advances in this area.Design/methodology/approachA systematic literature review on Sentiment Analysis for the Spanish language was conducted on recognized databases by the research community.FindingsResults show classification systems through three different approaches: Lexicon based, Machine Learning based and hybrid approaches. Additionally, different linguistic resources as Lexicon or corpus explicitly developed for the Spanish language were found.Originality/valueThis study provides academics and professionals, a review of advances in Sentiment Analysis for the Spanish language. Most reviews on Sentiment Analysis are for English, and other languages such as Chinese or Arabic, but no updated reviews were found for Spanish.

scholarly journals NATIONAL-CULTURAL AND LINGUISTIC FEATURES OF POLITICAL DISCOURSE (ON THE EXAMPLE OF SPEECHES BY THE FORMER PRESIDENT OF ARGENTINA CRISTINA FERNANDEZ DE KIRCHNER)

2020 ◽  
Vol 30 (3) ◽  
pp. 476-482
Author(s):  
D.A. Kushnereva
Keyword(s):  
Political Discourse ◽  
Cognitive Linguistics ◽  
Spanish Language ◽  
Linguistic Features ◽  
Historical Aspects ◽  
Political Linguistics ◽  
Theory Of Discourse

The article provides examples of cultural and linguistic features that a speaker uses in his/her speeches, and analyzes how they perform the function of persuasiveness, and, in particular, what strategies and tactics of speech manipulation are implemented due to these features. The material for the study was the speeches of the former president of Argentina, Cristina Fernandez de Kirchner, during the period from 2011 to 2017. In this article, special attention is paid to the linguistic features of the Spanish language of Argentina, the cultural and historical aspects of this country. The relevance of this work is determined by the need to study the mechanisms of speech manipulation, insufficiently developed on the material of the Spanish language. The results of the analysis presented in the article can be interesting for researchers in the field of political linguistics, the Spanish language of Argentina, the theory of discourse, cognitive linguistics, etc.

scholarly journals ASPECT-BASED SENTIMENT ANALYSIS OF POLITICAL DISCOURSE

2021 ◽  
Vol 7 (3) ◽  
pp. 6-22
Author(s):  
Elena G. Brunova ◽  
Yulia V. Bidulya ◽  
Alexander A. Gorbunov
Keyword(s):  
Sentiment Analysis ◽  
Political Discourse ◽  
Affective Computing ◽  
English Language ◽  
Linguistic Features ◽  
Text Document ◽  
User Comments ◽  
Linguistic Rules ◽  
Analysis System ◽  
Linguistic Units

The existing systems for accurate sentiment analysis are mainly based on statistical and mathematical principles. However, more promising are the works that are devoted to the study of the linguistic features of the evaluation expression. The results of this formalization can be applied both in the field of affective computing for further improvement of automatic systems and for linguistics and related sciences. The novelty of this study lies mainly in the development of an algorithm based on the identified linguistic rules. In addition, the research material is political discourse, which has not yet been studied enough by specialists of affective computing. The relevance of this work is justified by the growing need for categorization of information published on the Internet. The purpose of the study is to develop a system for machine sentiment analysis of English-language political texts, as well as to identify aspects and their distribution for subsequent use in enhancement. The article discusses the linguistic features of sentiment analysis and suggests a classification of linguistic units with sentiment potential in relation to levels of language structure. The results of an experiment on testing the operation of the sentiment analysis system, conducted on 300 news articles and user comments taken from reddit.com/r/politics, are also presented. The accuracy of the system is 92%. In addition, the selected 40 comments were manually marked up and tagged; during this process the expert identified 25 aspects. Furthermore, 3 formal patterns were identified in the distribution of aspect terms, which is necessary for creating an automatic system. The first peculiarity is that the aspect terms are repeated in two consecutive sentences. The second is that aspect terms are often the themes of sentences. Finally, the third — a high frequency of distribution of aspect terms at the beginning and end of the text (document) was revealed.

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