Data Detection, Tracking and Sentiment Analysis Based on Micro-Blog Data

2014 ◽  
Vol 1079-1080 ◽  
pp. 609-613
Author(s):  
Xue Mei Zhou ◽  
Yi Zhe Liu
Keyword(s):  
Information Technology ◽  
Public Opinion ◽  
Information Extraction ◽  
Sentiment Analysis ◽  
Data Detection ◽  
Extraction Technology ◽  
The Public ◽  
Opinion Analysis ◽  
Text Information ◽  
Information Media

With development of information technology and network technique, as information media micro-blog becomes more and more important. Micro-blog is noted for preeminent simple, convenient and interactivity. However with the help of micro-blog, fake information is rampant increasingly. The public opinion analysis of micro-blog data allows of no delay. This paper explicates the features of micro-blog text, and then describes text information extraction technology such as top detection, tracking in detail. The outcomes of information extraction can inform government department spot of internet public opinion in real time.

Crowdsourcing Investigations

2014 ◽  
Vol 6 (4) ◽  
pp. 60-75 ◽  
Author(s):  
Andrea H. Tapia ◽  
Nicolas J. LaLone
Keyword(s):  
Public Opinion ◽  
Sentiment Analysis ◽  
Ethical Dilemmas ◽  
The Internet ◽  
Social Approval ◽  
Online Groups ◽  
The Public ◽  
Large Numbers ◽  
Boston Marathon Bombing ◽  
Boston Marathon

In this paper the authors illustrate the ethical dilemmas that arise when large public investigations in a crisis are crowdsourced. The authors focus the variations in public opinion concerning the actions of two online groups during the immediate aftermath of the Boston Marathon Bombing. These groups collected and organized relief for victims, collected photos and videos taken of the bombing scene and created online mechanisms for the sharing and analysis of images collected online. They also used their large numbers and the affordances of the Internet to produce an answer to the question, “who was the perpetrator, and what kind of bomb was used?” The authors view their actions through public opinion, through sampling Twitter and applying a sentiment analysis to this data. They use this tool to pinpoint moments during the crisis investigation when the public became either more positively or negatively inclined toward the actions of the online publics. The authors use this as a surrogate, or proxy, for social approval or disapproval of their actions, which exposes large swings in public emotion as ethical lines are crossed by online publics.

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.

The “Blue Check” Communication on the Pandemia Vaccine Campaign

2022 ◽  
pp. 664-685
Author(s):  
Domenico Trezza ◽  
Miriam Di Lisio
Keyword(s):  
Content Analysis ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Critical Issue ◽  
The Public ◽  
Opinion Analysis ◽  
Attitudes And Perceptions

This chapter has the exploratory goal of understanding the attitudes and perceptions of 'verified' Twitter (VA) accounts about the COVID-19 vaccine campaign. Identifying their sentiment and opinion about it could therefore be crucial to the success of vaccination. A content analysis of tweets from the period December 24, 2020 to March 23, 2021 about the vaccine campaign in Italy was conducted to understand the semantic strategies used by VAs based on their orientation toward the vaccine, whether pro, anti, or neutral, and their possible motivations. Topic modeling allowed the authors to detect five prevalent themes and their associated words. A sentiment analysis and opinion analysis were performed on a smaller sample of tweets. The results suggest that 'authoritative' opinion about the vaccine has been very fragmented and not entirely positive, as expected. This could prove to be a critical issue in getting the vaccine positively accepted by the public.

A rule based approach for aspect extraction in hindi reviews

2021 ◽  
pp. 1-9
Author(s):  
Chinmayee Ojha ◽  
Manju Venugopalan ◽  
Deepa Gupta
Keyword(s):  
Public Opinion ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Fast Growth ◽  
Rule Based ◽  
The Public ◽  
Aspect Extraction ◽  
Automated Process ◽  
Rule Based Approach ◽  
Baseline Approach

Fast growth of technology and the tremendous growth of population has made millions of people to be active participants on social networking forums. The experiences shared by the participants on different websites is highly useful not only to customers to make decisions but also helps companies to maintain sustainability in businesses. Sentiment analysis is an automated process to analyze the public opinion behind certain topics. Identifying targets of user’s opinion from text is referred to as aspect extraction task, which is the most crucial and important part of Sentiment Analysis. The proposed system is a rule-based approach to extract aspect terms from reviews. A sequence of patterns is created based on the dependency relations between target and its nearby words. The system of rules works on a benchmark of dataset for Hindi shared by Akhtar et al., 2016. The evaluated results show that the proposed approach has significant improvement in extracting aspects over the baseline approach reported on the same dataset.

Crowdsourcing Investigations

Crowdsourcing ◽  
2019 ◽  
pp. 1433-1450
Author(s):  
Andrea H. Tapia ◽  
Nicolas J. LaLone
Keyword(s):  
Public Opinion ◽  
Sentiment Analysis ◽  
Ethical Dilemmas ◽  
The Internet ◽  
Social Approval ◽  
Online Groups ◽  
The Public ◽  
Large Numbers ◽  
Boston Marathon Bombing ◽  
Boston Marathon

In this paper the authors illustrate the ethical dilemmas that arise when large public investigations in a crisis are crowdsourced. The authors focus the variations in public opinion concerning the actions of two online groups during the immediate aftermath of the Boston Marathon Bombing. These groups collected and organized relief for victims, collected photos and videos taken of the bombing scene and created online mechanisms for the sharing and analysis of images collected online. They also used their large numbers and the affordances of the Internet to produce an answer to the question, “who was the perpetrator, and what kind of bomb was used?” The authors view their actions through public opinion, through sampling Twitter and applying a sentiment analysis to this data. They use this tool to pinpoint moments during the crisis investigation when the public became either more positively or negatively inclined toward the actions of the online publics. The authors use this as a surrogate, or proxy, for social approval or disapproval of their actions, which exposes large swings in public emotion as ethical lines are crossed by online publics.

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