Multilingual Twitter Sentiment Classification: The Role of Human Annotators

What are the limits of automated Twitter sentiment classification? We analyze a large set of manually labeled tweets in different languages, use them as training data, and construct automated classification models. It turns out that the quality of classification models depends much more on the quality and size of training data than on the type of the model trained. Experimental results indicate that there is no statistically significant difference between the performance of the top classification models. We quantify the quality of training data by applying various annotator agreement measures, and identify the weakest points of different datasets. We show that the model performance approaches the inter-annotator agreement when the size of the training set is sufficiently large. However, it is crucial to regularly monitor the self- and inter-annotator agreements since this improves the training datasets and consequently the model performance. Finally, we show that there is strong evidence that humans perceive the sentiment classes (negative, neutral, and positive) as ordered

Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

Peer reviewe

The use of user-generated content for business intelligence in tourism: insights from an analysis of Croatian hotels

Web-based peer review sites are gaining importance in travellers’ decision-making and provide information for destinations’ management. Textual reviews are especially important, but very extensive and hard to process. This article discusses the benefits of recent developments in computational linguistics and shows it can be used, based on a study of 18,000 reviews of Croatian hotels. Results show that numerical evaluation rarely provides sufficient information, while textual reviews reveal details about facilities’ competitive (dis)advantages. Being very extensive, the reviews are difficult to use. By applying computational linguistics the study illustrates how the information can be summarised and used in decision-making. The study extends the application of computational linguistics methodology to tourism literature and provides the first extensive analysis of TripAdvisor data for Croatia

Modelo de Sentiment Analysis para la clasificación de noticias en tiempo real en el Mercado de Valores de Buenos Aires

El proyecto de investigación en curso tiene como propósito mostrar que el monitoreo automático de noticias en tiempo real mediante algoritmos basados en Machine Learning puede servir como herramienta para la toma de decisiones de compra y venta de instrumentos financieros en el Mercado de Valores de Buenos Aires. Con este fin, recolectamos, analizamos y clasificamos opiniones extraídas de Twitter aplicando principios y técnicas de Sentiment Analysis relacionando aquellas noticias que generan un impacto directo sobre las acciones del mercado y aquellas que no lo hacen. Asimismo hemos diseñado un Lexicón de términos económicosfinanciero en español que nos permite asignar una etiqueta de polaridad “positiva” o “negativa” al corpus seleccionado. Basados en estas consideraciones, hemos obtenido resultados con buenos índices de precisión.Eje: Base de Datos y Minería de Dato

News Cohesiveness: an Indicator of Systemic Risk in Financial Markets

Motivated by recent financial crises significant research efforts have been put into studying contagion effects and herding behaviour in financial markets. Much less has been said about influence of financial news on financial markets. We propose a novel measure of collective behaviour in financial news on the Web, News Cohesiveness Index (NCI), and show that it can be used as a systemic risk indicator. We evaluate the NCI on financial documents from large Web news sources on a daily basis from October 2011 to July 2013 and analyse the interplay between financial markets and financially related news. We hypothesized that strong cohesion in financial news reflects movements in the financial markets. Cohesiveness is more general and robust measure of systemic risk expressed in news, than measures based on simple occurrences of specific terms. Our results indicate that cohesiveness in the financial news is highly correlated with and driven by volatility on the financial markets

"It All Ended in an Unsporting Way": Serbian Football and the Disintegration of Yugoslavia, 1989-2006

Part of a wider examination into football during the collapse of Eastern European Communism between 1989 and 1991, this article studies the interplay between Serbian football and politics during the period of Yugoslavia's demise. Research utilizing interviews with individuals directly involved in the Serbian game, in conjunction with contemporary Yugoslav media sources, indicates that football played an important proactive role in the revival of Serbian nationalism. At the same time the Yugoslav conflict, twinned with a complex transition to a market economy, had disastrous consequences for football throughout the territories of the former Yugoslavia. In the years following the hostilities the Serbian game has suffered decline, major financial hardship and continuing terrace violence, resulting in widespread nostalgia for the pre-conflict era

An Introduction to Social Semantic Web Mining & Big Data Analytics for Political Attitudes and Mentalities Research

The social web has become a major repository of social and behavioral data that is of exceptional interest to the social science and humanities research community. Computer science has only recently developed various technologies and techniques that allow for harvesting, organizing and analyzing such data and provide knowledge and insights into the structure and behavior or people on-line. Some of these techniques include social web mining, conceptual and social network analysis and modeling, tag clouds, topic maps, folksonomies, complex network visualizations, modeling of processes on networks, agent based models of social network emergence, speech recognition, computer vision, natural language processing, opinion mining and sentiment analysis, recommender systems, user profiling and semantic wikis. All of these techniques are briefly introduced, example studies are given and ideas as well as possible directions in the field of political attitudes and mentalities are given. In the end challenges for future studies are discussed

Uloga društvenih medija u mjerenju TV gledanosti

For many decades, traditional broadcast has been the main entertainment focal point in households. Like all media and entertainment industries, television has been altered by the internet and new technologies. The internet has made new forms of participatory communication possible and has increased the amount of interpersonal communication for individuals – audiences and users – providing opportunities to share, create and collaborate together. It offers manifold opportunities to communicate in all directions, as well as the opportunity to transmit and receive simultaneously all kinds of content and formats such as music, films, pictures and texts and enables the user to interact with links. The development of social media is more than a technical innovation: it sustains and influences all forms of social organisations. Besides (high speed) internet itself, wireless connectivity has created a comfortable environment for the usage of different devices. Smartphones, tablets and/or laptops are conquering households and invite (connected) usage while people watch TV; audiences divide their attention between a second and first screen, becoming a user and audience at the same time. It enables participation and social interaction within social media while watching TV. “Actions of the participatory audience appear in the value chain in several phases: when the audience is creating content, when they are editing or reediting the available content and when they are disseminating the content to other audience members” (Noguera Vivo et al., 2014, p. 181). This new participation of TV audiences in social media leads to an integration of TV consumption within the social media context. The “people formerly known as the audience are those who were on the receiving end of a media system that ran one way, in a broadcasting pattern, with high entry fees and a few firms competing to speak very loudly while the rest of the population listened in isolation from one another […]” (Rosen, 2006), the audience transformed into an active audience participating in the creation of (social) media content. The second screens enable virtual communication with friends about programs while watching and sharing what is liked and disliked, and television viewing coupled with audience interaction has gained popularity (Doughty, Rowland and Lawson, 2011). The audience can share, discuss, comment and vote about certain programs. Broadcasters and other suppliers offer applications accompanying TV consumption and solicit simultaneous usage. Audiences engage with the program and socialise with friends and communities around their favourite content. Television audience researchers discovered the internet as a source of audience data, and search for approaches to analyse online engagement of audiences. The main question of this work is to investigate if new data can be found and used in a systematic manner in addition to traditional television audience research methods. It was discovered that the relationship between television broadcasters and its social audience is the key to this approach. Traditional media such as TV broadcasters are still huge content providers and play a major role in the social media world, where content is shared and creates buzz and in addition users generate content themselves. Broadcasters are challenged to keep the relationship with and the attention of the viewer by building social interaction around the program. This is the prerequisite for the researcher to approach social media analysis in the context of television.Tradicionalno emitiranje je već desetljećima žarište zabave u kućanstvima. Kao i svi ostali mediji te industrije zabave, televizija je promijenjena zahvaljujući internetu i novim tehnologijama. Internet je omogućio nove forme komunikacije između sudionika te je povećao broj međuljutske komunikacije za pojedince – gledatelje i korisnike – tako što je omogućio prilike za zajedničkim dijeljenjem, stvaranjem i surađivanjem. Pruža mnoge prilike za komunikaciju u svim smjerovima, jednako kao i priliku za simultano slanje i primanje raznih vrsta sadržaja te formata kao što su muzika, filmovi, slike i tekstovi. Također omogućuje korisniku interakciju s web linkovima. Razvoj društvenih medija je više od tehnološke inovacije, ono podržava i utječe na sve oblike društvenih organizacija. Pored toga, sam internet (velike brzine) je uz bežično spajanje stvorio ugodnu okolinu za korištenje raznih uređaja. Pametni telefoni, tableti, i/ili laptopovi osvajaju kućanstva te pozivaju korisnika na online spajanje i korištenje interneta za vrijeme gledanja televizije pa tako gledatelji dijele svoju pozornost između dva ekrana, postajući na taj način istovremeni korisnici i gledatelji. Ovo omogućuje sudjelovanje te društvenu interakciju unutar društvenih medija tijekom gledanja televizije. “Djela uključenih gledatelja se pojavljuju u lancu vrijednosti u nekoliko faza: kada gledatelji stvaraju sadržaj, kada uređuju ili preuređuju dostupan sadržaj, te kada šire sadržaj drugim gledateljima. ” (Noguera Vivo et al., 2014, p. 181). Ovo novo sudjelovanje gledatelja na društvenim medijima vodi k integraciji gledanja televizije unutar konteksta društvenih medija. “Ljudi koji su prethodno prepoznati kao gledatelji su bili na primajućem kraju medijskog sustava koji se kretao u jednom smjeru, po strukturi emitiranja, uz visoke članarine te nekoliko tvrtki koje se natječu u tome da govore što glasnije dok ostatak populacije sluša u međusobnoj izolaciji […]” (Rosen, 2006), gledatelji su se pretvorili u aktivne gledatelje koji sudjeluju u stvaranju sadržaja (društvenih) medija. “Dodatni zasloni omogućavaju virtualnu komunikaciju s prijateljima o TV programima tijekom gledanja i dijeljenja sadržaja koji im se sviđa, odnosno ne sviđa, a i samo gledanje televizije s istovremenom interakcijom gledatelja postaje sve popularnije.” (Doughty, Rowland and Lawson, 2011). Gledatelji mogu dijeliti sadržaj, raspravljati, komentirati te glasati za određene TV programe. Televizijske kuće i ostali dobavljači nude aplikacije za praćenje korištenja usluge televizije te potiču njezino simultano korištenje. Gledatelji se uključuju u TV programe te raspravljaju s prijateljima i raznim zajednicama o njihovim najdražim sadržajima. Istražitelji koji prate gledanost televizije su prepoznali internet kao izvor podataka o gledateljima te istražuju pristupe za analizu online angažiranosti gledatelja. Potrebno je istražiti mogu li se pronaći novi podaci koji se mogu iskoristiti na sustavan način uz tradicionalne metode istraživanja gledanosti televizije. Otkriveno je da je ključ ovom pristupu sam odnos između televizijskih kuća i njihove publike, odnosno gledatelja. Tradicionalni mediji kao što su televizijske kuće se i dalje smatraju značajnim pružateljima sadržaja te igraju važnu ulogu u svijetu društvenih medija, gdje se dijele sadržaji koji stvaraju vijesti, i sadržaj pružaju sami korisnici. Izazov televizijskih kuća je da održavaju odnos s gledateljima te da imaju njihovu pozornost tako što će izgraditi društvenu interakciju oko TV programa. Ovo je preduvjet istražiteljima kako bi pristupili analizi društvenih medija u kontekstu televizije