Semantic orientation for polarity classification in Spanish reviews

2013 ◽  
Vol 40 (18) ◽  
pp. 7250-7257 ◽  
Author(s):  
M. Dolores Molina-González ◽  
Eugenio Martínez-Cámara ◽  
María-Teresa Martín-Valdivia ◽  
José M. Perea-Ortega
Keyword(s):  
Semantic Orientation ◽  
Polarity Classification

scholarly journals Lexicon-Based Methods for Sentiment Analysis

2011 ◽  
Vol 37 (2) ◽  
pp. 267-307 ◽  
Author(s):  
Maite Taboada ◽  
Julian Brooke ◽  
Milan Tofiloski ◽  
Kimberly Voll ◽  
Manfred Stede
Keyword(s):  
Sentiment Analysis ◽  
Subject Matter ◽  
Mechanical Turk ◽  
Classification Task ◽  
Main Subject ◽  
Unseen Data ◽  
Semantic Orientation ◽  
Polarity Classification

We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.

A Spanish semantic orientation approach to domain adaptation for polarity classification

2015 ◽  
Vol 51 (4) ◽  
pp. 520-531 ◽  
Author(s):  
M. Dolores Molina-González ◽  
Eugenio Martínez-Cámara ◽  
M. Teresa Martín-Valdivia ◽  
L. Alfonso Ureña-López
Keyword(s):  
Domain Adaptation ◽  
Semantic Orientation ◽  
Polarity Classification

Improving polarity classification of bilingual parallel corpora combining machine learning and semantic orientation approaches

2013 ◽  
Vol 64 (9) ◽  
pp. 1864-1877 ◽  
Author(s):  
José M. Perea-Ortega ◽  
M. Teresa Martín-Valdivia ◽  
L. Alfonso Ureña-López ◽  
Eugenio Martínez-Cámara
Keyword(s):  
Machine Learning ◽  
Parallel Corpora ◽  
Semantic Orientation ◽  
Polarity Classification

BUAP: Polarity Classification of Short Texts

2014 ◽  
Author(s):  
David Pinto ◽  
Darnes Vilariño ◽  
Saul Leon ◽  
Miguel Jasso ◽  
Cupertino Lucero
Keyword(s):  
Polarity Classification

Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis

2020 ◽  
pp. 235-242
Author(s):  
Farrikh Alzami ◽  
Erika Devi Udayanti ◽  
Dwi Puji Prabowo ◽  
Rama Aria Megantara
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Random Forest ◽  
Sentiment Analysis ◽  
Classification Performance ◽  
Document Preparation ◽  
Learning Models ◽  
Polarity Classification ◽  
Negative Sentiment ◽  
Machine Learning Models

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.

Prospects of anti-corruption education of the population in the Far-East: sociological analysis

2020 ◽  
Vol 92 (3) ◽  
pp. 161-176
Author(s):  
M. N. Zinyatova ◽  
◽  
Ye.A. Kleymenov ◽  
Keyword(s):  
Russian Federation ◽  
Financial Support ◽  
Far East ◽  
Practical Interest ◽  
Sociological Analysis ◽  
Management Decisions ◽  
The Far East ◽  
Semantic Orientation ◽  
The Russian Federation

On the basis of quantitative and qualitative expert sociological surveys, the article presents a model of anti-corruption education in Russia. This model is formed by seven main elements: basis, principles, subjects, objects, methods and means, content of materials (semantic orientation), indicators of the effectiveness of anti-corruption education. Comparing the obtained sociological data characterizing these elements with the corresponding elements of the anti-corruption mechanism enshrined in the current regulatory legal acts of the Russian Federation, the authors identified a number of inconsistencies. They concern, first of all, the principles, subjects of implementation of anti-corruption education, as well as indicators for assessing its effectiveness. For example, experts suggest using non-statutory principles of financial support and standardization of materials presented in the framework of such education when conducting anti-corruption education. At the same time, for the optimization of management decisions in the field of anti-corruption education, scientific and practical interest and contradictions identified within the obtained sociological data are of interest. Such contradictions are most clearly traced in relation to the subjects and objects of anti-corruption education.

Two New Large Corpora for Vietnamese Aspect-based Sentiment Analysis at Sentence Level

2021 ◽  
Vol 20 (4) ◽  
pp. 1-22
Author(s):  
Dang Van Thin ◽  
Ngan Luu-Thuy Nguyen ◽  
Tri Minh Truong ◽  
Lac Si Le ◽  
Duy Tin Vo
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Network Architectures ◽  
Low Resource ◽  
The Neural Network ◽  
Sentence Level ◽  
Push Forward ◽  
Polarity Classification ◽  
Learning Architectures ◽  
Single Approach

Aspect-based sentiment analysis has been studied in both research and industrial communities over recent years. For the low-resource languages, the standard benchmark corpora play an important role in the development of methods. In this article, we introduce two benchmark corpora with the largest sizes at sentence-level for two tasks: Aspect Category Detection and Aspect Polarity Classification in Vietnamese. Our corpora are annotated with high inter-annotator agreements for the restaurant and hotel domains. The release of our corpora would push forward the low-resource language processing community. In addition, we deploy and compare the effectiveness of supervised learning methods with a single and multi-task approach based on deep learning architectures. Experimental results on our corpora show that the multi-task approach based on BERT architecture outperforms the neural network architectures and the single approach. Our corpora and source code are published on this footnoted site. 1

Polarity Classification of Arabic Sentiments

2016 ◽  
Vol 11 (3) ◽  
pp. 32-49 ◽  
Author(s):  
Mohammed N. Al-Kabi ◽  
Heider A. Wahsheh ◽  
Izzat M. Alsmadi
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Operating Characteristic ◽  
Opinion Mining ◽  
Online Social Network ◽  
The Social ◽  
Polarity Classification ◽  
Arabic Sentiment Analysis ◽  
Modern Standard

Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.

Polarity classification using structure-based vector representations of text

2015 ◽  
Vol 74 ◽  
pp. 46-56 ◽  
Author(s):  
Alexander Hogenboom ◽  
Flavius Frasincar ◽  
Franciska de Jong ◽  
Uzay Kaymak
Keyword(s):  
Polarity Classification ◽  
Vector Representations

Spanish knowledge base generation for polarity classification from masses

2013 ◽  
Author(s):  
Arturo Montejo-Ráez ◽  
Manuel Carlos Díaz-Galiano ◽  
José Manuel Perea-Ortega ◽  
Luis Alfonso Ureña-López
Keyword(s):  
Knowledge Base ◽  
Polarity Classification
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