52,189 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
Design Ltd.: Renovated Myths for the Development of Socially Embedded Technologies
This paper argues that traditional and mainstream mythologies, which have
been continually told within the Information Technology domain among designers
and advocators of conceptual modelling since the 1960s in different fields of
computing sciences, could now be renovated or substituted in the mould of more
recent discourses about performativity, complexity and end-user creativity that
have been constructed across different fields in the meanwhile. In the paper,
it is submitted that these discourses could motivate IT professionals in
undertaking alternative approaches toward the co-construction of
socio-technical systems, i.e., social settings where humans cooperate to reach
common goals by means of mediating computational tools. The authors advocate
further discussion about and consolidation of some concepts in design research,
design practice and more generally Information Technology (IT) development,
like those of: task-artifact entanglement, universatility (sic) of End-User
Development (EUD) environments, bricolant/bricoleur end-user, logic of
bricolage, maieuta-designers (sic), and laissez-faire method to socio-technical
construction. Points backing these and similar concepts are made to promote
further discussion on the need to rethink the main assumptions underlying IT
design and development some fifty years later the coming of age of software and
modern IT in the organizational domain.Comment: This is the peer-unreviewed of a manuscript that is to appear in D.
Randall, K. Schmidt, & V. Wulf (Eds.), Designing Socially Embedded
Technologies: A European Challenge (2013, forthcoming) with the title
"Building Socially Embedded Technologies: Implications on Design" within an
EUSSET editorial initiative (www.eusset.eu/
Stance Detection in Web and Social Media: A Comparative Study
Online forums and social media platforms are increasingly being used to
discuss topics of varying polarities where different people take different
stances. Several methodologies for automatic stance detection from text have
been proposed in literature. To our knowledge, there has not been any
systematic investigation towards their reproducibility, and their comparative
performances. In this work, we explore the reproducibility of several existing
stance detection models, including both neural models and classical
classifier-based models. Through experiments on two datasets -- (i)~the popular
SemEval microblog dataset, and (ii)~a set of health-related online news
articles -- we also perform a detailed comparative analysis of various methods
and explore their shortcomings. Implementations of all algorithms discussed in
this paper are available at
https://github.com/prajwal1210/Stance-Detection-in-Web-and-Social-Media
Financial Reporting for Environmental and Social responsibility: A Normative Strategic Concept
Corporate responsibility demands that firms address environmental and social values in their firm’s policy and key performance indicators. These are integrated through strategic planning and require firms to merge the longer term environmental and social values with short term economic objectives and performance measures. Each firm’s strategy will differ. This paper provides a normative reporting concept to connect the financial implications associated with longer term planning for environmental and social values, with short term accounting reports. Reporting variants adapted from total cost assessment, life cycle costing, variable costing are integrated to offer upstream information based on a product segment view.Strategy, environmental reporting, life cycle costing, cost systems, multi-period accounting, multi-stage fixed costs.
Synthesizing Middle Grades Research on Cultural Responsiveness: The Importance of a Shared Conceptual Framework
In conducting a literature review of 133 articles on cultural responsiveness in middle level education, we identified a lack of shared definitions, theoretical frameworks, methodological approaches, and foci, which made it impossible to synthesize across articles. Using a conceptual framework that required: 1) clear definitions of terms; 2) a critically conscious stance; and 3) inclusion of the middle school concept, we identified 14 articles that met these criteria. We then mapped differences and convergences across these studies, which allowed us to identify the conceptual gaps that the field must address in order to have common definitions and understandings that enable synthesis across studies
Audio Event Detection using Weakly Labeled Data
Acoustic event detection is essential for content analysis and description of
multimedia recordings. The majority of current literature on the topic learns
the detectors through fully-supervised techniques employing strongly labeled
data. However, the labels available for majority of multimedia data are
generally weak and do not provide sufficient detail for such methods to be
employed. In this paper we propose a framework for learning acoustic event
detectors using only weakly labeled data. We first show that audio event
detection using weak labels can be formulated as an Multiple Instance Learning
problem. We then suggest two frameworks for solving multiple-instance learning,
one based on support vector machines, and the other on neural networks. The
proposed methods can help in removing the time consuming and expensive process
of manually annotating data to facilitate fully supervised learning. Moreover,
it can not only detect events in a recording but can also provide temporal
locations of events in the recording. This helps in obtaining a complete
description of the recording and is notable since temporal information was
never known in the first place in weakly labeled data.Comment: ACM Multimedia 201
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