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Register a new userMining Public Opinion about Economic Issues: Twitter and the U.S. Presidential Election
ترجمة الورقة البحثية بعنوان : استكشاف المعرفة من الرأي العام في المسائل الاقتصادية : تويتر و الانتخابات الرئاسية الأمريكية
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Damascus University مقالة
Publication date
2019
fields
Informatics Engineering
and research's language is
العربية
Created by
Ivan Abboud
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تعتبر استطلاعات الرأي الجسر الواصل بين الرأي العام و السياسيين أثناء الانتخابات. بالرغم من ذلك , فإن مسألة استقصاء الرأي لمعرفة ردة فعل الشعب بخصوص المسائل الاقتصادية , تعتبر محدودة, مكلفة و مستهلكة للوقت. تطور وسائل التواصل الاجتماعي في السنوات الأخيرة مثل تويتر (Twitter) , جعل من السهل على الأفراد مشاركة آرائهم الشخصية على نطاق واسع في مواضيع عديدة مثل الانتخابات. كما وفرت هذه الشبكات الاجتماعية منصة ضخمة لجمع المعطيات و البيانات المختلفة. تقترح هذه الورقة البحثية طريقة لاستكشاف الرأي العام و فهم النقاشات التي تدار في تويتر بخصوص المسائل الاقتصادية أثناء الانتخابات الرئاسية. تستخدم الطرق الحالية التي تتعلق بهذا البحث طرق متعددة لتعدين النصوص (Text mining) بشكل مستقل لتحليل الانتخابات و توقع الانتخابات ; في هذه الورقة البحثية سنقوم بدمج طريقتين لتعدين النصوص : تحليل المشاعر و نمذجة المواضيع. تم تطبيق الطريقة المقترحة بشكل فعال على ملايين التغريدات من تويتر لتحليل التخوفات الاقتصادية للشعب أثناء الانتخابات الرئاسية الأمريكية في عام 2012.
Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This paper proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election
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In this tutorial, we will show where we are and where we will be to those researchers interested in this topic. We divide this tutorial into three parts, including coarse-grained financial opinion mining, fine-grained financial opinion mining, and po
ssible research directions. This tutorial starts by introducing the components in a financial opinion proposed in our research agenda and summarizes their related studies. We also highlight the task of mining customers' opinions toward financial services in the FinTech industry, and compare them with usual opinions. Several potential research questions will be addressed. We hope the audiences of this tutorial will gain an overview of financial opinion mining and figure out their research directions.
We bring the data from the social networking site Twitter
pages, and then we have worked on cleaning and processing
operation to the text of for the classification process texts retrieved
contain a lot of noise and information is useful for the pr
ocess of
analyzing the views, such as advertisements and links and e-mail
addresses and the presence of many words that do not affect the
general orientation of the text, and then get all the publications in
the Twitter page and what are the comments about each tweets is
intended to know the proportion of supporters and opponents of
this publication.
We apply Naïve Bayes algorithm in classification, we had the
appropriate training, and after passing Posts and comments data
(opinions), we got good results on the ratio of supporters of the
post and the percentage of his opponents.
The public loan is one source of the state’s public revenues, and it does not happen
regularly.
The state usually resorts to this source in two cases:
The first case: When taxes reach the maximum degree, in other words, the taxation
power is exha
usted. In this situation, the state is not allowed to impose more taxes
otherwise this will lead to dangerous economic effects.
The second case: When taxes do not reach the maximum degree, but imposing them
can lead to violent reaction by taxpayers.
Therefore, the public loan constitutes an effective method in the hands of the state to
collect the savings that the taxes cannot obtain. Also, it is an important tool for the
distribution of the financial burden between the loaners/ and taxpayers./
The public loan has raised a controversy about its nature, the burden it causes, and its
appropriateness, and its impact/role in forming the national capital, etc…
Thus, these issues will be discussed in accordance with an appropriate search plan.
Stance detection, which aims to determine whether an individual is for or against a target concept, promises to uncover public opinion from large streams of social media data. Yet even human annotation of social media content does not always capture
stance'' as measured by public opinion polls. We demonstrate this by directly comparing an individual's self-reported stance to the stance inferred from their social media data. Leveraging a longitudinal public opinion survey with respondent Twitter handles, we conducted this comparison for 1,129 individuals across four salient targets. We find that recall is high for both Pro'' and Anti'' stance classifications but precision is variable in a number of cases. We identify three factors leading to the disconnect between text and author stance: temporal inconsistencies, differences in constructs, and measurement errors from both survey respondents and annotators. By presenting a framework for assessing the limitations of stance detection models, this work provides important insight into what stance detection truly measures.
Emotion detection from social media posts has attracted noticeable attention from natural language processing (NLP) community in recent years. The ways for obtaining gold labels for training and testing of the systems for automatic emotion detection
differ significantly from one study to another, and pose the question of reliability of gold labels and obtained classification results. This study systematically explores several ways for obtaining gold labels for Ekman's emotion model on Twitter data and the influence of the chosen strategy on the manual classification results.
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