scholarly journals Impact of Demonetization in India using Opinion Mining over Twitter Data

2020 ◽  
pp. 562-568
Author(s):  
Mrugendrasinh Rahevar ◽  
Martin Parmar ◽  
Rekha Karangiya
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Daily Basis ◽  
The Public ◽  
Everyday Activities ◽  
Large Numbers ◽  
The Everyday ◽  
The Government ◽  
Media Forum

In recent years, the utilization of Internet has turned out to be one of the everyday activities in our life. Social networks constitute a noteworthy segment of the Web and made an upheaval. It incorporates social media, forum conversations, blogs and micro-blogs like twitter. Due to this, large numbers of comments are produced on daily basis. So, nowadays most of the researchers or analyzers are concentrating on extracting significant data from social networks in order to understand the public viewpoint. This research has been reached out outside the computer science to cover other areas like business, political and social science. Hence, Sentiment analysis and Opinion mining are popular field of research in Data mining. This paper delineates various aspects of sentiment analysis in detail inclusive of important concepts, classification, process, importance, challenges and applications. The following paper presents experiment on sentiment analysis of public opinion on demonetization in India. Sentiment analysis is performed on tweets related to demonetization in India extracted from twitter. Polarity of the opinion is observed through the experimental analysis. Through the outcome of this analysis, the sentiments of the citizens that are determined help the government in improving their decisions and work for the welfare of the citizens.

scholarly journals Opinion Mining and Sentiment Analysis on Big Data

2020 ◽  
pp. 410-415
Author(s):  
Shruti Rajkumar Choudhary
Keyword(s):  
Artificial Intelligence ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Opinion Mining ◽  
Stock Exchange ◽  
Daily Basis ◽  
Text Data ◽  
The Public ◽  
Public Sentiment

<p>Opinion mining is extract subjective information from text data using tools such as NLP, text analysis etc. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product.In this project the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in terms of positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.</p>

Fear and loathing in the community: sexual offenders and desistance in a climate of risk and ‘extreme othering’

2016 ◽  
Author(s):  
Jacky Burrows
Keyword(s):  
Social Networks ◽  
Sex Offenders ◽  
Sexual Offenders ◽  
Sex Offender ◽  
The Public ◽  
Large Numbers ◽  
Master Status ◽  
The Media ◽  
The Government ◽  
Mainstream Society

This chapter focuses attention on sex offenders who, perhaps more than any other 'type' of offender, have been systematically vilified, demonised, and ostracised from mainstream society. The author argues that, for once, the public, the media, the government, and – worryingly – large numbers of professionals seem to be in agreement that such 'othering' is entirely right and proper in what are seen to be the larger interests of public protection. The author explores the implications of this deeply entrenched culture for ‘would-be desisters’ and suggests ways forward that offer individuals opportunities to uncouple from the ‘master status’ of sex offender and to build positive social networks.

scholarly journals Sentiment Analysis for Twitter Data

2020 ◽  
Vol 9 (7S) ◽  
pp. 98-101
Keyword(s):  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Daily Basis ◽  
Support Vector ◽  
Twitter Data ◽  
Internet Users ◽  
Political Views ◽  
Large Numbers ◽  
Machine Learning Approach

With the advancements in web technology and its growth, there's an incredible volume of information present everywhere on the net for internet users and plenty more data is generated on a daily basis. Internet emerged as place for exchanging ideas, sharing opinions, online learning and political views. Social networking sites such as Facebook, Twitter, are rapidly growing as the users are allowed to post and revel their views on various topics, and can discussion with different groups and communities, or post messages across the world. In the area of sentiment analysis large numbers of researchers are working. The main focus is on twitter data for sentiment analysis, that's helpful to research the info within the tweets,where opinions are heterogeneous, highly unstructured, and are either positive,or negative, or neutral.in many cases. In this paper, we provide a study and comparative analysis of existing techniques used for opinion mining through machine learning approach. Naive Bayes & Support Vector Machine, we provide research on twitter data.

scholarly journals Opinion Mining Analysis of Twitter Users of Particular Topic

2020 ◽  
pp. 102-108
Author(s):  
Sneha Naik ◽  
Mona Mulchandani
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Stock Exchange ◽  
Daily Basis ◽  
The Public ◽  
Public Sentiment ◽  
Twitter Users

Opinion mining consists of many different fields like natural language processing, text mining, decision making and linguistics. Opinion mining is a type of natural language processing for tracking the mood of the public about a particular product. Opinion mining, which is also called sentiment analysis, involves building a system to collect and categorize opinions about a product. Automated opinion mining often uses machine learning, a type of artificial intelligence (AI), to mine text for sentiment. This project addresses the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange.

scholarly journals Prediction of The Level of Public Trust in Government Policies in the 1st Quarter of The Covid 19 Pandemic using Sentiment Analysis

2021 ◽  
Vol 317 ◽  
pp. 05013
Author(s):  
Zahra Nur Aziza ◽  
Daniel Yeri Kristiyanto
Keyword(s):  
Sentiment Analysis ◽  
Government Policy ◽  
Research Result ◽  
Public Trust ◽  
The United States ◽  
Government Policies ◽  
Policy Changes ◽  
The Public ◽  
Large Numbers ◽  
The Government

The covid-19 pandemic has made changes in society, including Government policy. The policy changes led to mixing responses from the public, namely netizens. Netizen shares their opinion in social media, including Twitter. Their opinion can represent the public’s trust in the Government. Sentiment analysis analyses others’ opinions and categorises them into positive opinions, negative opinions, or neutral opinions. Sentiment analysis can analyze large numbers of opinions so that public opinion can be analyzed quickly. This paper explains how to analyze public trust using sentiment analysis and to use Naïve Bayes classification method to analyze sentiment. The data research was taken from Twitter in the first quarter of the Covid-19 pandemic, with around 3000 tweets. The tweets were related to Covid-19 and the Government from several countries such as the United States, Australia, Ireland, Switzerland, Italy, Philippines, Sri Lanka, Canada, Netherlands, United Kingdom, Germany, and Lebanon. This study aims to determine the level of public trust in the Government in the first quarter of the Covid-19 pandemic. The research result is expected to be used as a reference for the public policy stakeholders to determine future policies.

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.

scholarly journals Emancipating the Image: The Beijing Olympics, Regeneration, and the Power of Performance

2013 ◽  
Author(s):  
Carol Mei Barker
Keyword(s):  
Olympic Games ◽  
Public Image ◽  
Beijing Olympics ◽  
The Public ◽  
Susan Sontag ◽  
Counter Narrative ◽  
The Everyday ◽  
The World ◽  
The Government ◽  
Rapid Transformation

“In China, what makes an image true is that it is good for people to see it.” - Susan Sontag, On Photography, 1971 The Olympic Games gave the world an opportunity to read Beijing’s powerful image-text following thirty years of rapid transformation. David Harvey argues that this transformation has turned Beijing from “a closed backwater, to an open centre of capitalist dynamism.” However, in the creation of this image-text, another subtler and altogether very different image-text has been deliberately erased from the public gaze. This more concealed image-text offers a significant counter narrative on the city’s public image and criticises the simulacrum constructed for the 2008 Olympics, both implicitly and explicitly. It is the ‘everyday’ image-text of a disappearing city still in the process of being bulldozed to make way for the neoliberal world’s next megalopolis. It exists most prominently as a filmic image text; in film documentaries about a ‘real’ hidden Beijing just below the surface of the government sponsored ‘optical artefact.’ Film has thus become a key medium through which to understand and preserve a physical city on the verge of erasure.

scholarly journals Pinkas-notebook and pinkas-register book: evolution, structure, and composition

2021 ◽  
Vol 4 (5) ◽  
pp. 1199-1218
Author(s):  
Evgeniya D. Zarubina
Keyword(s):  
Academic Language ◽  
Jewish People ◽  
Uniform Structure ◽  
European Origin ◽  
The Public ◽  
Everyday Activities ◽  
The Everyday ◽  
History Of ◽  
Diverse Data ◽  
Communal Institutions

Minute books (pinkas) constitute one of the most valuable sources for studying the history of the Jewish communal institutions up to the 20th century. They comprise rich and diverse data on the everyday activities of the Jewish people. In the academic language, the word “pinkas” is applied not only to the communal minute books and minute books of the communal bodies but also to private minute books. The article deals with the development of this category of sources which evolved from private minute books dating back to at least the 11th century to the communal ones as well as the minute books of the communal bodies based on the dozen manuscript examples. These are mostly of European origin, however, with a few Eastern additions. This evolution process becomes visible as a result of the analysis of the manuscripts’ internal structure and composition. Special attention is paid to the techniques used to enforce this structure on codicological and paleographic levels. The data at hand suggest that at the beginning of the Modern period some of the minute books were shifted from private to the public domain. This was a response to the demand from the rapidly evolving communal institutions. To suit the widened audience of varying backgrounds the communal minute books compared to those for private use adopted a more uniform structure as well as with a set of “navigation” or referencing tools, such as captions written on margins. The early modern Italian communal minute books tend to be the most structured ones.

Sentiment Analysis in Crisis Situations for Better Connected Government

2021 ◽  
pp. 162-181
Author(s):  
Asdrúbal López Chau ◽  
David Valle-Cruz ◽  
Rodrigo Sandoval-Almazán
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Citizen Participation ◽  
Important Means ◽  
The Government ◽  
Made In ◽  
Crisis Situations

One of the pillars of connected government is citizen centricity: an approach in which citizen participation is essential. In Mexico, social networks are currently one of the most important means by which citizens express their needs and provide opinions to the government. The goal of this chapter is to contribute to citizen centricity by adapting the methodology of sentiment analysis of social media posts to an expanded version for crisis situations. The main difference in this approach from the normally accepted one is that instead of using pre-defined classes (positive and negative) for sentiments, the authors first determined the different data categories and then applied them to the classic process of sentiment analysis. This approach was tested using posts on Mexico's earthquake in 2017. They found that needs, demands, and claims made in the posts reflect sentiments in a better way, and this can help to improve the government-citizen connection.

Fake News Polarization for Sentiment Analysis

2022 ◽  
pp. 255-263
Author(s):  
Chirag Visani ◽  
Vishal Sorathiya ◽  
Sunil Lavadiya
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Opinion Mining ◽  
Easy Access ◽  
The Internet ◽  
Fake News ◽  
Social Media Networks ◽  
Sentiment Mining

The popularity of the internet has increased the use of e-commerce websites and news channels. Fake news has been around for many years, and with the arrival of social media and modern-day news at its peak, easy access to e-platform and exponential growth of the knowledge available on social media networks has made it intricate to differentiate between right and wrong information, which has caused large effects on the offline society already. A crucial goal in improving the trustworthiness of data in online social networks is to spot fake news so the detection of spam news becomes important. For sentiment mining, the authors specialise in leveraging Facebook, Twitter, and Whatsapp, the most prominent microblogging platforms. They illustrate how to assemble a corpus automatically for sentiment analysis and opinion mining. They create a sentiment classifier using the corpus that can classify between fake, real, and neutral opinions in a document.

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