Onyx: describing emotions on the web of data

There are several different standardised and widespread formats to represent emotions. However, there is no standard semantic model yet. This paper presents a new ontology, called Onyx, that aims to become such a standard while adding concepts from the latest Semantic Web models. In particular, the ontology focuses on the representation of Emotion Analysis results. But the model is abstract and inherits from previous standards and formats. It can thus be used as a reference representation of emotions in any future application or ontology. To prove this, we have translated resources from EmotionML representation to Onyx. We also present several ways in which developers could benefit from using this ontology instead of an ad-hoc presentation. Our ultimate goal is to foster the use of semantic technologies for emotion Analysis while following the Linked Data ideals

MAIA: an event-based modular architecture for intelligent agents

Online services are no longer isolated. The release of public APIs and technologies such as web hooks are allowing users and developers to access their information easily. Intelligent agents could use this information to provide a better user experience across services, connecting services with smart automatic. behaviours or actions. However, agent platforms are not prepared to easily add external sources such as web services, which hinders the usage of agents in the so-called Evented or Live Web. As a solution, this paper introduces an event-based architecture for agent systems, in accordance with the new tendencies in web programming. In particular, it is focused on personal agents that interact with several web services. With this architecture, called MAIA, connecting to new web services does not involve any modification in the platform

EUROSENTIMENT: Linked Data Sentiment Analysis

Sentiment and Emotion Analysis strongly depend on quality language resources, especially sentiment dictionaries. These resources are usually scattered, heterogeneous and limited to specific domains of appli- cation by simple algorithms. The EUROSENTIMENT project addresses these issues by 1) developing a common language resource representation model for sentiment analysis, and APIs for sentiment analysis services based on established Linked Data formats (lemon, Marl, NIF and ONYX) 2) by creating a Language Resource Pool (a.k.a. LRP) that makes avail- able to the community existing scattered language resources and services for sentiment analysis in an interoperable way. In this paper we describe the available language resources and services in the LRP and some sam- ple applications that can be developed on top of the EUROSENTIMENT LRP

Linguistic linked data for sentiment analysis

In this paper we describe the specification of amodel for the semantically interoperable representation of language resources for sentiment analysis. The model integrates "lemon", an RDF-based model for the specification of ontology-lexica (Buitelaar et al. 2009), which is used increasinglyfor the representation of language resources asLinked Data, with Marl, an RDF-based model for the representation of sentiment annotations (West-erski et al., 2011; Sánchez-Rada et al., 2013

A linked data approach to sentiment and emotion analysis of twitter in the financial domain

Sentiment analysis has recently gained popularity in the financial domain thanks to its capability to predict the stock market based on the wisdom of the crowds. Nevertheless, current sentiment indicators are still silos that cannot be combined to get better insight about the mood of different communities. In this article we propose a Linked Data approach for modelling sentiment and emotions about financial entities. We aim at integrating sentiment information from different communities or providers, and complements existing initiatives such as FIBO. The ap- proach has been validated in the semantic annotation of tweets of several stocks in the Spanish stock market, including its sentiment information

Generating Linked-Data based Domain-Specific Sentiment Lexicons from Legacy Language and Semantic Resources

We present a methodology for legacy language resource adaptation that generates domain-specific sentiment lexicons organized around domain entities described with lexical information and sentiment words described in the context of these entities. We explain the steps of the methodology and we give a working example of our initial results. The resulting lexicons are modelled as Linked Data resources by use of established formats for Linguistic Linked Data (lemon, NIF) and for linked sentiment expressions (Marl), thereby contributing and linking to existing Language Resources in the Linguistic Linked Open Data cloud

HESML: A scalable ontology-based semantic similarity measures library with a set of reproducible experiments and a replication dataset

This work is a detailed companion reproducibility paper of the methods and experiments proposed by Lastra-Díaz and García-Serrano in (2015, 2016) [56–58], which introduces the following contributions: (1) a new and efficient representation model for taxonomies, called PosetHERep, which is an adaptation of the half-edge data structure commonly used to represent discrete manifolds and planar graphs; (2) a new Java software library called the Half-Edge Semantic Measures Library (HESML) based on PosetHERep, which implements most ontology-based semantic similarity measures and Information Content (IC) models reported in the literature; (3) a set of reproducible experiments on word similarity based on HESML and ReproZip with the aim of exactly reproducing the experimental surveys in the three aforementioned works; (4) a replication framework and dataset, called WNSimRep v1, whose aim is to assist the exact replication of most methods reported in the literature; and finally, (5) a set of scalability and performance benchmarks for semantic measures libraries. PosetHERep and HESML are motivated by several drawbacks in the current semantic measures libraries, especially the performance and scalability, as well as the evaluation of new methods and the replication of most previous methods. The reproducible experiments introduced herein are encouraged by the lack of a set of large, self-contained and easily reproducible experiments with the aim of replicating and confirming previously reported results. Likewise, the WNSimRep v1 dataset is motivated by the discovery of several contradictory results and difficulties in reproducing previously reported methods and experiments. PosetHERep proposes a memory-efficient representation for taxonomies which linearly scales with the size of the taxonomy and provides an efficient implementation of most taxonomy-based algorithms used by the semantic measures and IC models, whilst HESML provides an open framework to aid research into the area by providing a simpler and more efficient software architecture than the current software libraries. Finally, we prove the outperformance of HESML on the state-of-the-art libraries, as well as the possibility of significantly improving their performance and scalability without caching using PosetHERep

Sentiment and Emotion Analysis in Social Networks: modeling and linking data, affects and people

El objetivo principal de esta tesis doctoral es mejorar el análisis de sentimientos y emociones de texto en redes sociales, aunando técnicas de procesamiento de lenguaje natural, datos enlazados y análisis de redes sociales. La investigación se divide en tres partes muy diferenciadas. Primero, se desarrolló un vocabulario semántico para describir emociones y procesos de análisis de sentimientos, alineado con la ontología de “procedencia” PROV-O. Este vocabulario permite seguir un enfoque de datos enlazados en el análisis de emociones, tanto en la anotación de recursos (datasets y lexicons), como en la publicación de servicios semánticos de análisis de emociones. Asimismo, se extendió el vocabulario de referencia para opiniones, Marl, para alinearlo con Prov-O. En segundo lugar, se han modelado los diferentes componentes de los servicios de análisis de sentimientos y emociones, así como los requisitos para crear servicios abiertos, interoperables y que se puedan combinar para lograr análisis avanzados. El resultado es un marco de desarrollo y modelado de servicios, enfocado en la modularidad. Además, se ha desarrollado una implementación de referencia que permite a crear y publicar servicios de análisis de sentimientos y emociones. En tercer lugar, se ha caracterizado el contexto social, que es el conjunto de información en una red social que complementa al mensaje, y que puede ser utilizado para mejorar el análisis de sentimientos del mensaje. También se ha desarrollado una taxonomía de enfoques de análisis de sentimientos basada en la forma en que el contexto social es construido y utilizado en el análisis. Seguidamente, se han investigado modelos de análisis de sentimientos que utilizan contexto social enriquecido mediante análisis de redes sociales. Por último, para explorar el potencial de las diferentes teorías sociales para el análisis de sentimientos se ha desarrollado una plataforma de simulación social, en la que se han implementado varios modelos de propagación de rumores y emociones. ----------ABSTRACT---------- The main goal of this thesis is to improve sentiment and emotion analysis of text in social media through a combination of natural language processing, linked data and social network analysis. To achieve this goal, we have divided our research into three parts. First, we developed a semantic vocabulary to describe emotions, emotion models and emotion analysis activities. This vocabulary enables a linked data approach to emotion analysis, including in the annotation and processing of resources (e.g., datasets and lexicons), and the development of public semantic emotion analysis services. We also extended the most popular vocabulary for opinions and sentiment, Marl, to include concepts of sentiment analysis activities. Secondly, we modeled the different components in a sentiment or emotion analysis service, as well as the requirements to create public and interoperable services that can be composed to produce advanced analyses. The result is a framework to model and develop modular services. We also developed a reference implementation of this framework, which can be used by researchers and developers to create and publish new sentiment and emotion analysis services. Thirdly, we studied and formalized the concept of social context, which is the information in a social network that accompanies a text message and can be used to improve the analysis of said text. We also developed a taxonomy of approaches to sentiment analysis based on how they gather social context and how they exploit it in the analysis. In addition to characterizing social context, we investigated several models of sentiment analysis that enrich social context through social network analysis. Lastly, we developed a social simulation platform, in which we modelled several rumor and emotion propagation behaviors

Onyx: A Linked Data approach to emotion representation

Extracting opinions and emotions from text is becoming increasingly important, especially since the advent of micro-blogging and social networking. Opinion mining is particularly popular and now gathers many public services, datasets and lexical resources. Unfortunately, there are few available lexical and semantic resources for emotion recognition that could foster the development of new emotion aware services and applications. The diversity of theories of emotion and the absence of a common vocabulary are two of the main barriers to the development of such resources. This situation motivated the creation of Onyx, a semantic vocabulary of emotions with a focus on lexical resources and emotion analysis services. It follows a linguistic Linked Data approach, it is aligned with the Provenance Ontology, and it has been integrated with the Lexicon Model for Ontologies (lemon), a popular RDF model for representing lexical entries. This approach also means a new and interesting way to work with different theories of emotion. As part of this work, Onyx has been aligned with EmotionML and WordNet-Affect

CRANK: A hybrid model for user and content sentiment classification using social context and community detection

Recent works have shown that sentiment analysis on social media can be improved by fusing text with social context information. Social context is information such as relationships between users and interactions of users with content. Although existing works have already exploited the networked structure of social context by using graphical models or techniques such as label propagation, more advanced techniques from social network analysis remain unexplored. Our hypothesis is that these techniques can help reveal underlying features that could help with the analysis. In this work, we present a sentiment classification model (CRANK) that leverages community partitions to improve both user and content classification. We evaluated this model one xisting datasets and compared it to other approaches