scholarly journals Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Hala Mulki ◽  
Hatem Haddad ◽  
Mourad Gridach ◽  
Ismail Babaoğlu
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Empirical Evaluation ◽  
Classification Performance ◽  
Analysis Framework ◽  
Named Entities ◽  
The Public ◽  
Considerable Impact ◽  
Arabic Sentiment Analysis

Social media reflects the attitudes of the public towards specific events. Events are often related to persons, locations or organizations, the so-called Named Entities (NEs). This can define NEs as sentiment-bearing components. In this paper, we dive beyond NEs recognition to the exploitation of sentiment-annotated NEs in Arabic sentiment analysis. Therefore, we develop an algorithm to detect the sentiment of NEs based on the majority of attitudes towards them. This enabled tagging NEs with proper tags and, thus, including them in a sentiment analysis framework of two models: supervised and lexicon-based. Both models were applied on datasets of multi-dialectal content. The results revealed that NEs have no considerable impact on the supervised model, while employing NEs in the lexicon-based model improved the classification performance and outperformed most of the baseline systems.

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.

scholarly journals Social media and sentiment in bioenergy consultation

2016 ◽  
Vol 10 (1) ◽  
pp. 87-98 ◽  
Author(s):  
Victoria Uren ◽  
Daniel Wright ◽  
James Scott ◽  
Yulan He ◽  
Hassan Saif
Keyword(s):  
Social Media ◽  
New Media ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Public Consultation ◽  
Short Paper ◽  
Consultation Process ◽  
Content Type ◽  
The Public ◽  
The Uk

Purpose – This paper aims to address the following challenge: the push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organizations towards energy development projects. Design/methodology/approach – This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised and illustrated using a sample of tweets containing the term “bioenergy”. Findings – Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable methodology, which bioenergy companies should be using to measure public opinion in the consultation process. Preliminary analysis shows promising results. Research limitations/implications – Analysis is preliminary and based on a small dataset. It is intended only to illustrate the potential of sentiment analysis and not to draw general conclusions about the bioenergy sector. Social implications – Social media have the potential to open access to the consultation process and help bioenergy companies to make use of waste for energy developments. Originality/value – Opinion mining, though established in marketing and political analysis, is not yet systematically applied as a planning consultation tool. This is a missed opportunity.

Sentiment Analysis as a Service: A Social Media Based Sentiment Analysis Framework

2017 ◽  
Author(s):  
Kashif Ali ◽  
Hai Dong ◽  
Athman Bouguettaya ◽  
Abdelkarim Erradi ◽  
Rachid Hadjidj
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Analysis Framework

scholarly journals Kecenderungan Tanggapan Masyarakat terhadap Ekonomi Indonesia berbasis Lexicon Based Sentiment Analysis

2019 ◽  
Vol 3 (4) ◽  
pp. 268
Author(s):  
Muhammad Zidny Nafan ◽  
Andika Elok Amalia
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
The Public

Sentiment analysis aims to find opinions, identify sentiments expressed, and then classify their polarity values. One method of sentiment analysis is Lexicon-based. This study implements the Lexicon based sentiment analysis to analyze the polarity of public responses to the topic of the development of "the Indonesian economy". The dataset is collected from social media from 2017 to 2019. Preprocessing used is folding cases, deleting newline characters, changing non-standard words, deleting mentions, deleting hashtags, removing URL strings, changing word negation, and translating text into English with TextBlob library. Then extract the sentiment values from adjectives, adverbs, nouns, and verbs found in the text. Based on the results of sentiment analysis, it can be seen that there are 63.6% positive responses from the public to the development of the Indonesian economy, 7.4% negative responses, and 29% neutral.

scholarly journals Analysis of Social Media Users Sentiments against Omnibus Law Based on Hashtags on Twitter

SISTEMASI ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 197
Author(s):  
Okta Fanny ◽  
Heri Suroyo
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Main Topic ◽  
Accuracy Score ◽  
Test Results ◽  
Bayes Classifier ◽  
The Public ◽  
Average Accuracy ◽  
Positive Sentiment ◽  
Negative Sentiment

From the research that has been done, it can be concluded that Sentiment Analysis can be used to know the sentiment of the public, especially Twitter netizens against omnibus law. After the sentiment analysis, it looks neutral artmen with the largest percentage of 55%, then positive sentiment by 35% and negative sentiment by 10%. The results of the analysis showed that the Naïve Bayes Classifier method provides classification test results with accuracy in Hashtag Pro with an average accuracy score of 92.1%, precision values with an average of 94.8% and recall values with an average of 90.7%. While Hashtag Counter For data classification, with an average accuracy value of 98.3%, precision value with an average of 97.6% and recall value with an average of 98.7%. The result of text cloud analysis conducted on a combination of hashtags both Hashtag pros and Hashtags cons, the dominant word appears is Omnibus Law which means that all hashtags in scrap is really discussing the main topic that is about Omnibus Law

scholarly journals A Comprehensive Analysis of Approaches for Sentiment Analysis Using Twitter Data on COVID-19 Vaccines

2021 ◽  
Vol 2 (2) ◽  
pp. 1-10
Author(s):  
Amrita Mishra ◽  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Comprehensive Analysis ◽  
Social Media Data ◽  
The Public ◽  
Opinion Analysis ◽  
Twitter Data ◽  
Media Data

Sentiment Analysis has paved routes for opinion analysis of masses over unrestricted territorial limits. With the advent and growth of social media like Twitter, Facebook, WhatsApp, Snapchat in today’s world, stakeholders and the public often takes to expressing their opinion on them and drawing conclusions. While these social media data are extremely informative and well connected, the major challenge lies in incorporating efficient Text Classification strategies which not only overcomes the unstructured and humongous nature of data but also generates correct polarity of opinions (i.e. positive, negative, and neutral). This paper is a thorough effort to provide a brief study about various approaches to SA including Machine Learning, Lexicon Based, and Automatic Approaches. The paper also highlights the comparison of positive, negative, and neutral tweets of the Sputnik V, Moderna, and Covaxin vaccines used for preventive and emergency use of COVID-19 disease.

scholarly journals Empirical Evaluation of Shallow and Deep Learning Classifiers for Arabic Sentiment Analysis

2022 ◽  
Vol 21 (1) ◽  
pp. 1-25
Author(s):  
Ali Bou Nassif ◽  
Abdollah Masoud Darya ◽  
Ashraf Elnagar
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Empirical Evaluation ◽  
Detailed Comparison ◽  
Learning Models ◽  
Similar Work ◽  
Learning Classifiers ◽  
Arabic Sentiment Analysis ◽  
Gated Recurrent Units

This work presents a detailed comparison of the performance of deep learning models such as convolutional neural networks, long short-term memory, gated recurrent units, their hybrids, and a selection of shallow learning classifiers for sentiment analysis of Arabic reviews. Additionally, the comparison includes state-of-the-art models such as the transformer architecture and the araBERT pre-trained model. The datasets used in this study are multi-dialect Arabic hotel and book review datasets, which are some of the largest publicly available datasets for Arabic reviews. Results showed deep learning outperforming shallow learning for binary and multi-label classification, in contrast with the results of similar work reported in the literature. This discrepancy in outcome was caused by dataset size as we found it to be proportional to the performance of deep learning models. The performance of deep and shallow learning techniques was analyzed in terms of accuracy and F1 score. The best performing shallow learning technique was Random Forest followed by Decision Tree, and AdaBoost. The deep learning models performed similarly using a default embedding layer, while the transformer model performed best when augmented with araBERT.

Applying sentiment analytics to examine social media crises: a case study of United Airline's crisis in 2017

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xin Tian ◽  
Wu He ◽  
Feng-Kwei Wang
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Social Media Data ◽  
Content Type ◽  
The Public ◽  
Response Strategies ◽  
Related Information ◽  
Valence And Arousal ◽  
Public Reaction ◽  
Media Data

PurposeIn recent years, social media crises occurred more and more often, which negatively affect the reputations of individuals, businesses and communities. During each crisis, numerous users either participated in online discussion or widely spread crisis-related information to their friends and followers on social media. By applying sentiment analysis to study a social media crisis of airline carriers, the purpose of this research is to help companies take measure against social media crises.Design/methodology/approachThis study used sentiment analytics to examine a social media crisis related to airline carriers. The arousal, valence, negative, positive and eight emotional sentiments were applied to analyze social media data collected from Twitter.FindingsThis research study found that social media sentiment analysis is useful to monitor public reaction after a social media crisis arises. The sentiment results are able to reflect the development of social media crises quite well. Proper and timely response strategies to a crisis can mitigate the crisis through effective communication with the customers and the public.Originality/valueThis study used the Affective Norms of English Words (ANEW) dictionary to classify the words in social media data and assigned the words with two elements to measure the emotions: valence and arousal. The intensity of the sentiment determines the public reaction to a social media crisis. An opinion-oriented information system is proposed as a solution for resolving a social media crisis in the paper.

scholarly journals How do Canadian public health agencies respond to the COVID-19 emergency using social media: a protocol for a case study using content and sentiment analysis

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e041818
Author(s):  
Anita Kothari ◽  
Lyndsay Foisey ◽  
Lorie Donelle ◽  
Michael Bauer
Keyword(s):  
Public Health ◽  
Social Media ◽  
Research Ethics ◽  
Sentiment Analysis ◽  
Health Agency ◽  
Message Content ◽  
Public Health Agencies ◽  
The Public ◽  
Board Review

IntroductionKeeping Canadians safe requires a robust public health (PH) system. This is especially true when there is a PH emergency, like the COVID-19 pandemic. Social media, like Twitter and Facebook, is an important information channel because most people use the internet for their health information. The PH sector can use social media during emergency events for (1) PH messaging, (2) monitoring misinformation, and (3) responding to questions and concerns raised by the public. In this study, we ask: what is the Canadian PH risk communication response to the COVID-19 pandemic in the context of social media?Methods and analysisWe will conduct a case study using content and sentiment analysis to examine how provinces and provincial PH leaders, and the Public Health Agency of Canada and national public heath leaders, engage with the public using social media during the first wave of the pandemic (1 January–3 September 2020). We will focus specifically on Twitter and Facebook. We will compare findings to a gold standard during the emergency with respect to message content.Ethics and disseminationWestern University’s research ethics boards confirmed that this study does not require research ethics board review as we are using social media data in the public domain. Using our study findings, we will work with PH stakeholders to collaboratively develop Canadian social media emergency response guideline recommendations for PH and other health system organisations. Findings will also be disseminated through peer-reviewed journal articles and conference presentations.

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