25,759 research outputs found
Semantic Sentiment Analysis of Twitter Data
Internet and the proliferation of smart mobile devices have changed the way
information is created, shared, and spreads, e.g., microblogs such as Twitter,
weblogs such as LiveJournal, social networks such as Facebook, and instant
messengers such as Skype and WhatsApp are now commonly used to share thoughts
and opinions about anything in the surrounding world. This has resulted in the
proliferation of social media content, thus creating new opportunities to study
public opinion at a scale that was never possible before. Naturally, this
abundance of data has quickly attracted business and research interest from
various fields including marketing, political science, and social studies,
among many others, which are interested in questions like these: Do people like
the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about
the Brexit? Answering these questions requires studying the sentiment of
opinions people express in social media, which has given rise to the fast
growth of the field of sentiment analysis in social media, with Twitter being
especially popular for research due to its scale, representativeness, variety
of topics discussed, as well as ease of public access to its messages. Here we
present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the
Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition.
201
Quantifying discrepancies in opinion spectra from online and offline networks
Online social media such as Twitter are widely used for mining public
opinions and sentiments on various issues and topics. The sheer volume of the
data generated and the eager adoption by the online-savvy public are helping to
raise the profile of online media as a convenient source of news and public
opinions on social and political issues as well. Due to the uncontrollable
biases in the population who heavily use the media, however, it is often
difficult to measure how accurately the online sphere reflects the offline
world at large, undermining the usefulness of online media. One way of
identifying and overcoming the online-offline discrepancies is to apply a
common analytical and modeling framework to comparable data sets from online
and offline sources and cross-analyzing the patterns found therein. In this
paper we study the political spectra constructed from Twitter and from
legislators' voting records as an example to demonstrate the potential limits
of online media as the source for accurate public opinion mining.Comment: 10 pages, 4 figure
Active learning in annotating micro-blogs dealing with e-reputation
Elections unleash strong political views on Twitter, but what do people
really think about politics? Opinion and trend mining on micro blogs dealing
with politics has recently attracted researchers in several fields including
Information Retrieval and Machine Learning (ML). Since the performance of ML
and Natural Language Processing (NLP) approaches are limited by the amount and
quality of data available, one promising alternative for some tasks is the
automatic propagation of expert annotations. This paper intends to develop a
so-called active learning process for automatically annotating French language
tweets that deal with the image (i.e., representation, web reputation) of
politicians. Our main focus is on the methodology followed to build an original
annotated dataset expressing opinion from two French politicians over time. We
therefore review state of the art NLP-based ML algorithms to automatically
annotate tweets using a manual initiation step as bootstrap. This paper focuses
on key issues about active learning while building a large annotated data set
from noise. This will be introduced by human annotators, abundance of data and
the label distribution across data and entities. In turn, we show that Twitter
characteristics such as the author's name or hashtags can be considered as the
bearing point to not only improve automatic systems for Opinion Mining (OM) and
Topic Classification but also to reduce noise in human annotations. However, a
later thorough analysis shows that reducing noise might induce the loss of
crucial information.Comment: Journal of Interdisciplinary Methodologies and Issues in Science -
Vol 3 - Contextualisation digitale - 201
Mining the Demographics of Political Sentiment from Twitter Using Learning from Label Proportions
Opinion mining and demographic attribute inference have many applications in
social science. In this paper, we propose models to infer daily joint
probabilities of multiple latent attributes from Twitter data, such as
political sentiment and demographic attributes. Since it is costly and
time-consuming to annotate data for traditional supervised classification, we
instead propose scalable Learning from Label Proportions (LLP) models for
demographic and opinion inference using U.S. Census, national and state
political polls, and Cook partisan voting index as population level data. In
LLP classification settings, the training data is divided into a set of
unlabeled bags, where only the label distribution in of each bag is known,
removing the requirement of instance-level annotations. Our proposed LLP model,
Weighted Label Regularization (WLR), provides a scalable generalization of
prior work on label regularization to support weights for samples inside bags,
which is applicable in this setting where bags are arranged hierarchically
(e.g., county-level bags are nested inside of state-level bags). We apply our
model to Twitter data collected in the year leading up to the 2016 U.S.
presidential election, producing estimates of the relationships among political
sentiment and demographics over time and place. We find that our approach
closely tracks traditional polling data stratified by demographic category,
resulting in error reductions of 28-44% over baseline approaches. We also
provide descriptive evaluations showing how the model may be used to estimate
interactions among many variables and to identify linguistic temporal
variation, capabilities which are typically not feasible using traditional
polling methods
Pre Processing of Twitter's Data for Opinion Mining in Political Context
AbstractIn the wake of political activism among youth in particular and the whole population in general, everyone is not only eager to share their political orientation but equally curious regarding the voice of the masses. As a known notion, the perfect orifice to this emerging need of political activism can be found on social media platforms, from where the numerous aspects of public opinion can be captured easily. These sites have begun to have a large impact on how people think and act. It is a known phenomenon that public opinion is the largest indicator of success and failure of political parties and is a direct reflection of the party's reign. Where increased sharing of public feedback has increased awareness and promoted accountability, it has also created chaos and confusion for many. Using Twitter, the most popular micro blogging platform, this paper aims to give a method to ease and smooth the task of opinion mining with the help of linguistic analysis and opinion classifiers, which will together determine positive, negative and neutral sentiments for the political parties of Pakistan. A method is provided which pre-processes the raw data of twitter and comparison of two classification techniques to classify this data. That will aspire to capture a snapshot of current political scenario to promote the spirit of accountability, self-analysis and improvement in among Pakistani politicians. Moreover, with this we aim to give general public an important consolidated voice in the realm of politics
Opini Masyarakat Twitter terhadap Kandidat Bakal Calon Presiden Republik Indonesia Tahun 2024
Registration for the 2024 presidential candidates began at the end of 2023, but the euphoria of the supporters of the 2024 presidential candidates began to be felt from the beginning of 2022. Several survey institutions released public opinions regarding several prospective 2024 presidential candidates. One of the approaches taken in the survey was by conducting direct interviews with the public. However, political dynamics can change the results of political surveys at great expense. Public opinion about the 2024 presidential candidates cannot only be acquired through direct interviews. Public opinion acquisition can also be done through social media such as Twitter. This article aims to find out public opinion on the candidates for the 2024 presidential candidate on Twitter social media. This article uses a Twitter dataset and data analysis tools using orange data mining. The crawling dataset was carried out using the hashtags #capres2024 and #presiden2024 and the keywords anies baswedan, prabowo subianto and ganjar pranowo with 10,000 tweet data in content written in Indonesian. Text preprocessing includes transformation, tokenization, filtering and normalization applied to data before analysis is carried out with topic modeling and sentiment towards the presidential candidates. The results of the word cloud analysis show a very high level of popularity for candidate Ganjar Pranowo, but the results of the sentiment analysis show that Ganjar Pranowo has a negative sentiment
Sentiment Analysis of Twitter Data
Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, emotions, political and religious views from written language about personality, product or event and determined whether they are viewed positively or negatively. Our project will involve collection of data from web resources such as twitter by using Hadoop and intend to derive useful inferences and recommendations. From the evaluation of this study it can be concluded that the proposed machine learning and natural language processing techniques are an effective and practical methods for sentiment analysis
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