1,926 research outputs found
Numeral Understanding in Financial Tweets for Fine-grained Crowd-based Forecasting
Numerals that contain much information in financial documents are crucial for
financial decision making. They play different roles in financial analysis
processes. This paper is aimed at understanding the meanings of numerals in
financial tweets for fine-grained crowd-based forecasting. We propose a
taxonomy that classifies the numerals in financial tweets into 7 categories,
and further extend some of these categories into several subcategories. Neural
network-based models with word and character-level encoders are proposed for
7-way classification and 17-way classification. We perform backtest to confirm
the effectiveness of the numeric opinions made by the crowd. This work is the
first attempt to understand numerals in financial social media data, and we
provide the first comparison of fine-grained opinion of individual investors
and analysts based on their forecast price. The numeral corpus used in our
experiments, called FinNum 1.0 , is available for research purposes.Comment: Accepted by the 2018 IEEE/WIC/ACM International Conference on Web
Intelligence (WI 2018), Santiago, Chil
White, Man, and Highly Followed: Gender and Race Inequalities in Twitter
Social media is considered a democratic space in which people connect and
interact with each other regardless of their gender, race, or any other
demographic factor. Despite numerous efforts that explore demographic factors
in social media, it is still unclear whether social media perpetuates old
inequalities from the offline world. In this paper, we attempt to identify
gender and race of Twitter users located in U.S. using advanced image
processing algorithms from Face++. Then, we investigate how different
demographic groups (i.e. male/female, Asian/Black/White) connect with other. We
quantify to what extent one group follow and interact with each other and the
extent to which these connections and interactions reflect in inequalities in
Twitter. Our analysis shows that users identified as White and male tend to
attain higher positions in Twitter, in terms of the number of followers and
number of times in user's lists. We hope our effort can stimulate the
development of new theories of demographic information in the online space.Comment: In Proceedings of the IEEE/WIC/ACM International Conference on Web
Intelligence (WI'17). Leipzig, Germany. August 201
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
A Semantics-Based Measure of Emoji Similarity
Emoji have grown to become one of the most important forms of communication
on the web. With its widespread use, measuring the similarity of emoji has
become an important problem for contemporary text processing since it lies at
the heart of sentiment analysis, search, and interface design tasks. This paper
presents a comprehensive analysis of the semantic similarity of emoji through
embedding models that are learned over machine-readable emoji meanings in the
EmojiNet knowledge base. Using emoji descriptions, emoji sense labels and emoji
sense definitions, and with different training corpora obtained from Twitter
and Google News, we develop and test multiple embedding models to measure emoji
similarity. To evaluate our work, we create a new dataset called EmoSim508,
which assigns human-annotated semantic similarity scores to a set of 508
carefully selected emoji pairs. After validation with EmoSim508, we present a
real-world use-case of our emoji embedding models using a sentiment analysis
task and show that our models outperform the previous best-performing emoji
embedding model on this task. The EmoSim508 dataset and our emoji embedding
models are publicly released with this paper and can be downloaded from
http://emojinet.knoesis.org/.Comment: This paper is accepted at Web Intelligence 2017 as a full paper, In
2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). Leipzig,
Germany: ACM, 201
Mining Domain-Specific Thesauri from Wikipedia: A case study
Domain-specific thesauri are high-cost, high-maintenance, high-value knowledge structures. We show how the classic thesaurus structure of terms and links can be mined automatically from Wikipedia. In a comparison with a professional thesaurus for agriculture we find that Wikipedia contains a substantial proportion of its concepts and semantic relations; furthermore it has impressive coverage of contemporary documents in the domain. Thesauri derived using our techniques capitalize on existing public efforts and tend to reflect contemporary language usage better than their costly, painstakingly-constructed manual counterparts
Introduction to the Special Section on Reputation in Agent Societies
This special section includes papers from the 'Reputation in Agent Societies' workshop held as part of 2004 IEEE/WIC/ACM International Joint Conference on Intelligent Agent Technology (IAT'04) and Web Intelligence (WI'04), September 20, 2004 in Beijing, China. The purpose of this workshop was to promote multidisciplinary collaboration for Reputation Systems modeling and implementation. Reputation is increasingly at the centre of attention in many fields of science and domains of application, including economics, organisations science, policy-making, (e-)governance, cultural evolution, social dilemmas, socio-dynamics, innofusion, etc. However, the result of all this attention is a great number of ad hoc models and little integration of instruments for the implementation, management and optimisation of reputation. On the one hand, entrepreneurs and administrators manage corporate and firm reputation without contributing to or accessing a solid, general and integrated body of scientific knowledge on the subject matter. On the other hand, software designers believe they can design and implement online reputation reporting systems without investigating what the properties, requirements and dynamics of reputation in natural societies are and why it evolved. We promoted the workshop and this special section with the hope of setting the first steps in the direction of a new, cross-disciplinary approach to reputation, accounting for the social cognitive mechanisms and processes that support it and working towards t a consensus on essential guidelines for designing or shaping reputation technologies.Reputation, Agent Systems
Ontology-based specific and exhaustive user profiles for constraint information fusion for multi-agents
Intelligent agents are an advanced technology utilized in Web Intelligence. When searching information from a distributed Web environment, information is retrieved by multi-agents on the client site and fused on the broker site. The current information fusion techniques rely on cooperation of agents to provide statistics. Such techniques are computationally expensive and unrealistic in the real world. In this paper, we introduce a model that uses a world ontology constructed from the Dewey Decimal Classification to acquire user profiles. By search using specific and exhaustive user profiles, information fusion techniques no longer rely on the statistics provided by agents. The model has been successfully evaluated using the large INEX data set simulating the distributed Web environment
Guidelines Towards Better Participation of Older Adults in Software Development Processes using a new SPIRAL Method and Participatory Approach
This paper presents a new method of engaging older participants in the
process of application and IT solutions development for older adults for
emerging IT and tech startups. A new method called SPIRAL (Support for
Participant Involvement in Rapid and Agile software development Labs) is
proposed which adds both sustainability and flexibility to the development
process with older adults. This method is based on the participatory approach
and user empowerment of older adults with the aid of a bootstrapped Living Lab
concept and it goes beyond well established user-centered and empathic design.
SPIRAL provides strategies for direct involvement of older participants in the
software development processes from the very early stage to support the agile
approach with rapid prototyping, in particular in new and emerging startup
environments with limited capabilities, including time, team and resources
- …