Sentiment Analysis of Sinhala News Comments

2021 ◽  
Vol 20 (4) ◽  
pp. 1-23
Author(s):  
Surangika Ranathunga ◽  
Isuru Udara Liyanage
Keyword(s):  
Sentiment Analysis ◽  
Word Embedding ◽  
Semantic Features ◽  
End User ◽  
Language Resources ◽  
Low Resource ◽  
Statistical Classifiers ◽  
User Comments ◽  
Basic Language ◽  
Analysis System

Sinhala is a low-resource language, for which basic language and linguistic tools have not been properly defined. This affects the development of NLP-based end-user applications for Sinhala. Thus, when implementing NLP tools such as sentiment analyzers, we have to rely only on language-independent techniques. This article presents the use of such language-independent techniques in implementing a sentiment analysis system for Sinhala news comments. We demonstrate that for low-resource languages such as Sinhala, the use of recently introduced word embedding models as semantic features can compensate for the lack of well-developed language-specific linguistic or language resources, and text classification with acceptable accuracy is indeed possible using both traditional statistical classifiers and Deep Learning models. The developed classification models, a corpus of 8.9 million tokens extracted from Sinhala news articles and user comments, and Sinhala Word2Vec and fastText word embedding models are now available for public use; 9,048 news comments annotated with POSITIVE/NEGATIVE/NEUTRAL polarities have also been released.

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.

scholarly journals Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews

2021 ◽  
Author(s):  
Asad Khattak ◽  
Muhammad Zubair Asghar ◽  
Zain Ishaq ◽  
Waqas Haider Bangyal ◽  
Ibrahim A Hameed
Keyword(s):  
Sentiment Analysis ◽  
User Reviews ◽  
Analysis System

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

Sentiment analysis of low-carbon travel APP user comments based on deep learning

2021 ◽  
Vol 44 ◽  
pp. 101014
Author(s):  
Xing-Min Lin ◽  
Chun-Heng Ho ◽  
Lu-Ting Xia ◽  
Ruo-Yi Zhao
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Low Carbon ◽  
User Comments

scholarly journals MoSa: A Modeling and Sentiment Analysis System for Mobile Application Big Data

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 115 ◽  
Author(s):  
Yaocheng Zhang ◽  
Wei Ren ◽  
Tianqing Zhu ◽  
Ehoche Faith
Keyword(s):  
Sentiment Analysis ◽  
Interaction Design ◽  
Mobile Application ◽  
Situational Awareness ◽  
Mobile Internet ◽  
The Public ◽  
Public Sentiment ◽  
High Altitude Area ◽  
Sentiment Dictionary ◽  
Analysis System

The development of mobile internet has led to a massive amount of data being generated from mobile devices daily, which has become a source for analyzing human behavior and trends in public sentiment. In this paper, we build a system called MoSa (Mobile Sentiment analysis) to analyze this data. In this system, sentiment analysis is used to analyze news comments on the THAAD (Terminal High Altitude Area Defense) event from Toutiao by employing algorithms to calculate the sentiment value of the comment. This paper is based on HowNet; after the comparison of different sentiment dictionaries, we discover that the method proposed in this paper, which use a mixed sentiment dictionary, has a higher accuracy rate in its analysis of comment sentiment tendency. We then statistically analyze the relevant attributes of the comments and their sentiment values and discover that the standard deviation of the comments’ sentiment value can quickly reflect sentiment changes among the public. Besides that, we also derive some special models from the data that can reflect some specific characteristics. We find that the intrinsic characteristics of situational awareness have implicit symmetry. By using our system, people can obtain some practical results to guide interaction design in applications including mobile Internet, social networks, and blockchain based crowdsourcing.

Engaging Your Feelings: Emotion Contagion and Public Engagement on Nonprofit Organizations’ Facebook Sites

2021 ◽  
pp. 089976402110573
Author(s):  
Zongchao Cathy Li ◽  
Yi Grace Ji ◽  
Weiting Tao ◽  
Zifei Fay Chen
Keyword(s):  
United States ◽  
Nonprofit Organizations ◽  
Sentiment Analysis ◽  
Public Engagement ◽  
Negative Emotions ◽  
The United States ◽  
Computer Assisted ◽  
Diffusion Effect ◽  
User Comments ◽  
Emotion Contagion

This study investigated nonprofit organizations’ (NPOs) emotion-based content strategies on Facebook and publics’ engagement behaviors. More than 52,000 Facebook posts and corresponding comments were collected from the top 100 NPOs in the United States. The emotion-carrying status and valence of the messages were analyzed with computer-assisted sentiment analysis procedures. Results confirmed emotion-carrying posts and posts with negative emotions led to increased public engagement as indexed by the volumes of likes, shares, and comments. The presence of emotions and valence of the NPOs’ posts were also found to have a diffusion effect on user comments.

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