History-based Self-Organizing Traffic Lights

Managing traffic in cities is nowadays a complex problem involving considerable physical and economical resources. Multi-agent Systems (MAS) consist of a set of distributed, usually co-operating, agents that act autonomously. The traffic in a city can be simulated by a MAS with different agents, cars and traffic lights, that interact to obtain an overall goal: to reduce average waiting times for the traffic users. In this paper, we describe an agent-based simulator to model traffic in cities.Using this simulator, we present a self-organizing solution to efficiently manage urban traffic. We compare our proposal with recent approaches, providing better results than classical and alternative self-organizing methods, with lower resources and investments

New methodologies to evaluate the consistency of emoji sentiment lexica and alternatives to generate them in a fully automatic unsupervised way

Sentiment analysis aims at detecting sentiment polarities in unstructured Internet information. A relevant part of this information for that purpose, emojis, whose use in Twitter has grown considerably in these years, deserves attention. However, every time a new version of Unicode is released, finding out the sentiment users wish to express with a new emoji is challenging. In [KNSSM15], an Emoji Sentiment Ranking lexicon from manual annotations of messages in different languages was presented. The quality of these annotations affects directly the quality of possible generated emoji sentiment lexica (high quality corresponds to high self-agreement and inter-agreement). In many cases, the creators of the datasets do not provide any quality metrics, so it is necessary to use another strategy to detect this issue. Therefore, we propose an automatic approach to identify and manage inconsistent manual sentiment annotations. Then, relying on a new approach to generate emoji sentiment lexica of good quality, we compare two such lexica with lexica created from manually annotated datasets with poor and high qualities

Differentiating users by language and location estimation in sentiment analisys of informal text during major public events

In recent years there has been intense work on the analysis of social media to support marketing campaigns. A proper methodology for sentiment analysis is a crucial asset in this regard. However, when monitoring major public events the behaviour or social media users may be strongly biased by punctual actions of the participating characters and the sense of group belonging, which is typically linked to specific geographical areas. In this paper, we present a solution combining a location prediction methodology with an unsupervised technique for sentiment analysis to assess automatically the engagement of social network users in different countries during an event with worldwide impact. As far as the authors know, this is the first time such techniques are jointly considered. We demonstrate that the technique is coherent with the intrinsic disposition of individual users to typical actions of the characters participating in the events, as well as with the sense of group belonging.Ministerio de Economía, Industria y Competitividad | Ref. TEC2016-76465-C2-2-RXunta de Galicia | Ref. GRC2014/046Xunta de Galicia | Ref. ED341D R2016/01

Lexicon for natural language generation in spanish adapted to alternative and augmentative communication

In this paper we present Elsa, the first lexicon for Spanish with morphological, syntactic and semantic information automatically generated from a well-known pictogram resource and especially tailored for Augmentative and Alternative Communication (AAC). This lexicon, focusing on that specific icon set widely used within AAC applications, is motivated by the need to improve Natural Language Generation (NLG) systems to aid people who have been diagnosed to suffer from communication disorders. In addition, we design an automatic lexicon extension procedure by means of a training process to complete the linguistic data. For this we used a dataset composed of novels and tales in Spanish, with pictogram representations, since the lexicon is meant for AAC applications for children with disabilities. Moreover, we provide the algorithms used to build our lexicon and a use case of Elsa within an NLG system to observe the usability of our proposal.Agencia Estatal de Investigación | Ref. TEC2016-76465-C2-2-RXunta de Galicia | Ref. GRC2014/04

Evaluation of online emoji description resources for sentiment analysis purposes

Emoji sentiment analysis is a relevant research topic nowadays, for which emoji sentiment lexica are key assets. Manual annotation affects directly their quality (where high quality usually corresponds to high self-agreement and inter-agreement). In this work we present an unsupervised methodology to evaluate emoji sentiment lexica generated from online resources, based on a correlation analysis between a gold standard and the scores resulting from the sentiment analysis of the emoji descriptions in those resources. We consider in our study four such online resources of emoji descriptions: Emojipedia, Emojis.wiki, CLDR emoji character annotations and iEmoji. These resources provide knowledge about real (intended) emoji meanings from different author approaches and perspectives. We also present the automatic creation of a joint lexicon where the sentiment of a given emoji is obtained by averaging its scores from the unsupervised analysis of all the resources involved. The results for the joint lexicon are highly promising, suggesting that valuable subjective information can be inferred from authors’ descriptions in online resources.Agencia Estatal de Investigación | Ref. TEC2016-76465-C2-2-RXunta de Galicia | Ref. GRC2018/05

Creating emoji lexica from unsupervised sentiment analysis of their descriptions

Online media, such as blogs and social networking sites, generate massive volumes of unstructured data of great interest to analyze the opinions and sentiments of individuals and organizations. Novel approaches beyond Natural Language Processing are necessary to quantify these opinions with polarity metrics. So far, the sentiment expressed by emojis has received little attention. The use of symbols, however, has boomed in the past four years. About twenty billion are typed in Twitter nowadays, and new emojis keep appearing in each new Unicode version, making them increasingly relevant to sentiment analysis tasks. This has motivated us to propose a novel approach to predict the sentiments expressed by emojis in online textual messages, such as tweets, that does not require human effort to manually annotate data and saves valuable time for other analysis tasks. For this purpose, we automatically constructed a novel emoji sentiment lexicon using an unsupervised sentiment analysis system based on the definitions given by emoji creators in Emojipedia. Additionally, we automatically created lexicon variants by also considering the sentiment distribution of the informal texts accompanying emojis. All these lexica are evaluated and compared regarding the improvement obtained by including them in sentiment analysis of the annotated datasets provided by Kralj Novak, Smailovic, Sluban and Mozetic (2015). The results confirm the competitiveness of our approach.Agencia Estatal de Investigación | Ref. TEC2016-76465-C2-2-RXunta de Galicia | Ref. GRC2014/046Xunta de Galicia | Ref. ED341D R2016/01

A library for automatic natural language generation of Spanish texts

In this article we present a novel system for natural language generation (nlg) of Spanish sentences from a minimum set of meaningful words (such as nouns, verbs and adjectives) which, unlike other state-of-the-art solutions, performs the nlg task in a fully automatic way, exploiting both knowledge-based and statistical approaches. Relying on its linguistic knowledge of vocabulary and grammar, the system is able to generate complete, coherent and correctly spelled sentences from the main word sets presented by the user. The system, which was designed to be integrable, portable and efficient, can be easily adapted to other languages by design and can feasibly be integrated in a wide range of digital devices. During its development we also created a supplementary lexicon for Spanish, aLexiS, with wide coverage and high precision, as well as syntactic trees from a freely available definite-clause grammar. The resulting nlg library has been evaluated both automatically and manually (annotation). The system can potentially be used in different application domains such as augmentative communication and automatic generation of administrative reports or news.Xunta de Galicia | Ref. ED341D R2016/012Xunta de Galicia | Ref. GRC 2014/046Ministerio de Economía, Industria y Competitividad | Ref. TEC2016-76465-C2-2-

Automatic detection of relevant information, predictions and forecasts in financial news through topic modelling with Latent Dirichlet Allocation

Financial news items are unstructured sources of information that can be mined to extract knowledge for market screening applications. They are typically written by market experts who describe stock market events within the context of social, economic and political change. Manual extraction of relevant information from the continuous stream of finance-related news is cumbersome and beyond the skills of many investors, who, at most, can follow a few sources and authors. Accordingly, we focus on the analysis of financial news to identify relevant text and, within that text, forecasts and predictions. We propose a novel Natural Language Processing (NLP) system to assist investors in the detection of relevant financial events in unstructured textual sources by considering both relevance and temporality at the discursive level. Firstly, we segment the text to group together closely related text. Secondly, we apply co-reference resolution to discover internal dependencies within segments. Finally, we perform relevant topic modelling with Latent Dirichlet Allocation (LDA) to separate relevant from less relevant text and then analyse the relevant text using a Machine Learning-oriented temporal approach to identify predictions and speculative statements. Our solution outperformed a rule-based baseline system. We created an experimental data set composed of 2,158 financial news items that were manually labelled by NLP researchers to evaluate our solution. Inter-agreement Alpha-reliability and accuracy values, and ROUGE-L results endorse its potential as a valuable tool for busy investors. The ROUGE-L values for the identification of relevant text and predictions/forecasts were 0.662 and 0.982, respectively. To our knowledge, this is the first work to jointly consider relevance and temporality at the discursive level. It contributes to the transfer of human associative discourse capabilities to expert systems through the combination of multi-paragraph topic segmentation and co-reference resolution to separate author expression patterns, topic modelling with LDA to detect relevant text, and discursive temporality analysis to identify forecasts and predictions within this text. Our solution may have compelling applications in the financial field, including the possibility of extracting relevant statements on investment strategies to analyse authors’ reputations.Universidade de Vigo/CISUGXunta de Galicia | Ref. ED481B-2021-118Xunta de Galicia | Ref. ED481B-2022-09

Adaptation of Augmentative and Alternative Communicators through the study of interactions with high-tech solution users

Augmentative and Alternative Communication (aac) strategies ease communication tasks for people who require accessible solutions. These strategies are usually addressed by technological solutions such as mobile applications. This research seeks clues on the development of such applications by analyzing user interactions with Android application PictoDroid Lite, an aac communicator. This study considered a data set containing more than 85,000 interactions of users from more than 50 countries. The goal was to identify the primary needs reflected in the users’ behavior and how these applications handle them, providing other researchers and developers with relevant information about how users interact with these applications. We detected areas of improvement regarding the adaptation to users’ needs in terms of profiling, smart suggestions, and time habits.Xunta de Galicia | Ref. ED481A-2023-090Xunta de Galicia | Ref. ED481B-2022-093Xunta de Galicia | Ref. ED431C 2022/04Agencia Estatal de Investigación | Ref. TED2021-130824B-C2

A System for Automatic English Text Expansion

This work was supported in part by the Mineco, Spain, under Grant TEC2016-76465-C2-2-R, in part by the Xunta de Galicia, Spain, under Grant GRC-2018/53 and Grant ED341D R2016/012, and in part by the University of Vigo Travel Grant to visit the CLAN Research Group, University of Aberdeen, U.K.Peer reviewedPublisher PD