Detecting Sarcasm in Multimodal Social Platforms

Sarcasm is a peculiar form of sentiment expression, where the surface sentiment differs from the implied sentiment. The detection of sarcasm in social media platforms has been applied in the past mainly to textual utterances where lexical indicators (such as interjections and intensifiers), linguistic markers, and contextual information (such as user profiles, or past conversations) were used to detect the sarcastic tone. However, modern social media platforms allow to create multimodal messages where audiovisual content is integrated with the text, making the analysis of a mode in isolation partial. In our work, we first study the relationship between the textual and visual aspects in multimodal posts from three major social media platforms, i.e., Instagram, Tumblr and Twitter, and we run a crowdsourcing task to quantify the extent to which images are perceived as necessary by human annotators. Moreover, we propose two different computational frameworks to detect sarcasm that integrate the textual and visual modalities. The first approach exploits visual semantics trained on an external dataset, and concatenates the semantics features with state-of-the-art textual features. The second method adapts a visual neural network initialized with parameters trained on ImageNet to multimodal sarcastic posts. Results show the positive effect of combining modalities for the detection of sarcasm across platforms and methods.Comment: 10 pages, 3 figures, final version published in the Proceedings of ACM Multimedia 201

Characterization of Local Attitudes Toward Immigration Using Social Media

Migration is a worldwide phenomenon that may generate different reactions in the population. Attitudes vary from those that support multiculturalism and communion between locals and foreigners, to contempt and hatred toward immigrants. Since anti-immigration attitudes are often materialized in acts of violence and discrimination, it is important to identify factors that characterize these attitudes. However, doing so is expensive and impractical, as traditional methods require enormous efforts to collect data. In this paper, we propose to leverage Twitter to characterize local attitudes toward immigration, with a case study on Chile, where immigrant population has drastically increased in recent years. Using semi-supervised topic modeling, we situated 49K users into a spectrum ranging from in-favor to against immigration. We characterized both sides of the spectrum in two aspects: the emotions and lexical categories relevant for each attitude, and the discussion network structure. We found that the discussion is mostly driven by Haitian immigration; that there are temporal trends in tendency and polarity of discussion; and that assortative behavior on the network differs with respect to attitude. These insights may inform policy makers on how people feel with respect to migration, with potential implications on communication of policy and the design of interventions to improve inter-group relations.Comment: 8 pages, accepted at Latin American Web Congress 2019 (co-located with The Web Conference

Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM

Sentiment analysis on large-scale social media data is important to bridge the gaps between social media contents and real world activities including political election prediction, individual and public emotional status monitoring and analysis, and so on. Although textual sentiment analysis has been well studied based on platforms such as Twitter and Instagram, analysis of the role of extensive emoji uses in sentiment analysis remains light. In this paper, we propose a novel scheme for Twitter sentiment analysis with extra attention on emojis. We first learn bi-sense emoji embeddings under positive and negative sentimental tweets individually, and then train a sentiment classifier by attending on these bi-sense emoji embeddings with an attention-based long short-term memory network (LSTM). Our experiments show that the bi-sense embedding is effective for extracting sentiment-aware embeddings of emojis and outperforms the state-of-the-art models. We also visualize the attentions to show that the bi-sense emoji embedding provides better guidance on the attention mechanism to obtain a more robust understanding of the semantics and sentiments

Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN

Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets

Programa intensivo ERASMUS: TOPCART. Documentación Geométrica del Patrimonio (memoria de actividades 2010-2011)

[EN] Data contained in this record come from the following accademic activity (from which it is possible to locate additional records related with the Monastery):● LDGP_inv_002: "Intensive Program ERASMUS: TOPCART. Geometric Documentation of the Heritage (administrative and academic documentation)", http://hdl.handle.net/10810/9906[ES] Los datos de este registro provienen de la una actividad académica que también aparece descrita en el repositorio y desde donde se puede acceder a otros trabajos relacionados con el Monasterio:● LDGP_inv_002: "Programa intensivo ERASMUS: TOPCART. Documentación Geométrica del Patrimonio (documentación administrativa y académica)", http://hdl.handle.net/10810/9906[EN] The main objective this project is looking for is the exchange of practical methodologies, in topics related with the measure and representation of heritage, between teachers and specially students from different countries. For the achievement of this aim we expect the participation of a group of about 30 students and 8 lecturers from Germany, Italy, Greece, Lithuania and Spain.Activities will be focused on the development of concrete projects in documentation of heritage, specifically in the San Prudencio Monastery (La Rioja, Spain). In this site, digital techniques for the acquisition of geometric information from GPS equipment, surveying total stations, laser scanner and photogrammetry systems, will be put into practice.Obtained data will be processed as follows: first of all, they will be documented by adding necessary metadata in order to ensure their use in the future, then, they will be treated to obtain cartographic representations and virtual models which can be distributed on the Internet.As results we expect: metric data of the monument, graphic models for difussion and collaboration partnertships.[ES] El objetivo principal que se persigue en este proyecto es el intercambio de metodológico práctico, en materias afines a la medida y la representación del patrimonio, entre profesores y fundamentalmente alumnos, de diferentes países. Para la consecución de este fin se espera la participación de un grupo de aproximadamente 25 alumnos y 8 profesores de (Alemania, Italia, Grecia, Lituania y España).Las actividades se centrarán en el desarrollo de proyectos concretos de documentación de elementos patrimoniales, en concreto el apartado práctico se desarrollará en el Monasterio de San Prudencio (La Rioja, España). En el se aplicarán técnicas digitales de registro de información geométrica, constituidas por receptores GPS, estaciones totales topográficas, escáneres láser y sistemas fotogramétricos.Los datos obtenidos serán tratados de la siguiente manera: en primer lugar serán documentados, mediante la adición de la metainformación necesaria para garantizar su utilidad a lo largo del tiempo, seguidamente serán procesados con el fin de obtener las representaciones cartográficas y modelos virtuales de representación que puedan ser difundidas por medio de Internet.Como resultados se pretenden: un conjunto de registros métricos del momento de la intervención, modelos gráficos de difusión y finalmente relaciones de colaboración interpersonal e interinstitucional.European Commission, DG Education and Culture (Erasmus 2009-1-ES1-ERAIP-0013, 2010-1-ES1-ERA10-0024); Organismo Autónomo Programas Educativos Europeos (OAPEE); Gobierno de La Rioja (Spain); Universidad de La Rioja; Clavijo City Council; Logroño City Council; Ilustre Colegio de Ingenieros Técnicos en Topografía (Delegación de La Rioja)[ES] Memoria de proyecto (PDF) [es el último fichero de la lista, el enlace directo es https://addi.ehu.es/bitstream/10810/7053/1053/ldgp_mem011-1_Clavijo_SanPrudencio.pdf] + 11 imágenes de la visita preliminar en abril de 2009, en formato JPEG + 19 nubes de puntos en formato txt (comprimido en ZIP junto a un fichero de metadatos y una imagen que sirve de croquis y que también se presenta suelta) + 27 fotografías tomadas desde un helicóptero radicontrolado en 2011 por el grupo H (JPEG) + 18 fotografías métricas del edificio en forma de -L- tomadas desde el Sur + 13 fotografías métricas del edificio en forma de -L- tomadas desde el Este + 95 fotografías métricas del interior del edificio en forma de -L- (JPEG) + 35 fotografías métricas tomadas desde el cerro que se encuentra al sur (JPEG) + 8 fotografías métricas que forman 4 pares estereoscópicos (2 del grupo B y 2 del grupo D) (JPEG) + 183 fotografías métricas que forman 91 tripletas (grupos B, C y D) (JPEG). [NOTA: este registro no está cerrado, se irán incorporando nuevos materiales de forma progresiva][EN] General report (PDF) [it is the last file of the list, the direct link is https://addi.ehu.es/bitstream/10810/7053/1053/ldgp_mem011-1_Clavijo_SanPrudencio.pdf] + 11 pictures taken during the preliminary visit in April 2009 (JPEG format) + 19 point clouds in plain text (compressed in a ZIP file together with a file with metadata and an image PNG as sketch, these image are also presented on their own) + 27 photographs taken from a remote-controlled helicopter for the group H in 2011(JPEG) + 18 metric pictures of the L-shaped building taken from the South (JPEG) + 13 metric pictures of the L-shaped building taken from the East (JPEG) + 95 metric pictures of the inside part of the L-shaped building (JPEG) + 35 metric photographs taken from the hill opposite in the Southern + 8 metric photographs in four stereopairs (2 from group B and 2 from group D) (JPEG) + 183 metric photographs arranged in 91 triplets from groups B, C and D (JPEG). [NOTE: this record is not closed, more data will be uploaded progressively

Preliminary study of dune deposits of the northern Lanzarote Islets. Palaeoenviromental implications

The dune deposits in the northern Lanzarote islets (Islotes) show the climatic changes (by sea level oscillations) en the last 40.000 years BP. Stratigraphic and faunistic analysis, as well as radiometric data, suggest that there are at last five sedimentary stages, ocurred during sea level regressions. The last episode has been dated at the end of the isotopic stage 2 and could be related with the last negative sea level oscillation (Younger Dryas episode). The fossil terrestrial gastropoda species are the same as the present ones in Islotes, Lanzarote and Fuerteventura, with few examples of exclusive taxon

Deciding among fake, satirical, objective and legitimate news: A multi-label classification system

Currently, the widespread of fake news has raised on the politicalclass and society members in general, increasing concerns aboutthe potential of misinformation that can be propagated, appearingon the center of the debate about election results around the world.On the other hand, satirical news has an entertaining purpose andare mistakenly put on the same boat of objective fake news. Inthis work, we address the differences between objectivity and legitimacy of news documents, treating each article as having twoconceptual classes: objective/satirical and legitimate/fake. Thus, wepropose a Decision Support System (DSS) based on a text miningpipeline and a set of novel textual features that uses multi-labelmethods for classifying news articles on those two domains. Forvalidating the approach, a set of multi-label methods was evaluatedwith a combination of different base classifiers and then comparedto a multi-class approach. Results reported our DSS as proper (0.80F1-score) in addressing the scenario of misleading news from challenging perspective of multi-label modeling, outperforming themulti-class methods (0.71 F1-score) over a real-life news datasetcollected from several portals of news