A Semantic Approach for Outlier Detection in Big Data Streams

In recent years, the world faced a big revolution in data generation and collection technologies. The volume, velocity and veracity of data have changed drastically and led to new types of challenges related to data analysis, modeling and prediction. One of the key challenges is related to the semantic analysis of textual data especially in big data streams settings. The existing solutions focus on either topic analysis or the sentiment analysis. Moreover, the semantic outlier detection over data streams as one of the key problems in data mining and data analysis fields has less focus. In this paper, we introduce a new concept of semantic outlier through which the topic of the textual data is considered as the primary content of the data stream while the sentiment is considered as the context in which the data has been generated and affected. Also, we propose a framework for semantic outlier detection in big data streams which incorporates the contextual detection concepts. The advantage of the proposed concept is that it incorporates both topic and sentiment analysis into one single process; while at the same time the framework enables the implementation of different algorithms and approaches for semantic analysis

DETECTING PANIC POTENTIAL IN SOCIAL MEDIA TWEETS

A high degree of real-time interconnectedness can aid information transmission, particularly in disaster situations. However, it can have substantial negative consequences when information is emotionally laden and transmits these emotions, particularly the emotion of panic, to the individual across social media in an already grave situation. Prior research has shown that information laden with emotion spreads through social network faster than otherwise. Hence, we highlight the need to understand and curtail potentially panic-causing information, without compromising on good quality information from being available for effective crisis communication and management. With this research, we present the necessity of detecting the panic potential of social media messages, and aim to address two research questions: What are the features, and metrics necessary, to compute and evaluate the panic potential of a social media message (respectively)? Our planned analysis takes the case of the Munich shooting incident, 2016, based on user tweets immediately after the incident. Different features and evaluation metrics are proposed and discussed. The work aims to detect panic potential of messages in social media networks during disasters

Sentiment analysis during Hurricane Sandy in emergency response

Sentiment analysis has been widely researched in the domain of online review sites with the aim of generating summarized opinions of users about different aspects of products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users during disaster events. Identifying such sentiments from online social networking sites can help emergency responders understand the dynamics of the network, e.g., the main users' concerns, panics, and the emotional impacts of interactions among members. In this paper, we perform a sentiment analysis of tweets posted on Twitter during the disastrous Hurricane Sandy and visualize online users' sentiments on a geographical map centered around the hurricane. We show how users' sentiments change according not only to their locations, but also based on the distance from the disaster. In addition, we study how the divergence of sentiments in a tweet posted during the hurricane affects the tweet retweetability. We find that extracting sentiments during a disaster may help emergency responders develop stronger situational awareness of the disaster zone itself

A Semantic Approach for Outlier Detection in Big Data Streams

In recent years, the world faced a big revolution in data generation and collection technologies. The volume, velocity and veracity of data have changed drastically and led to new types of challenges related to data analysis, modeling and prediction. One of the key challenges is related to the semantic analysis of textual data especially in big data streams settings. The existing solutions focus on either topic analysis or the sentiment analysis. Moreover, the semantic outlier detection over data streams as one of the key problems in data mining and data analysis fields has less focus. In this paper, we introduce a new concept of semantic outlier through which the topic of the textual data is considered as the primary content of the data stream while the sentiment is considered as the context in which the data has been generated and affected. Also, we propose a framework for semantic outlier detection in big data streams which incorporates the contextual detection concepts. The advantage of the proposed concept is that it incorporates both topic and sentiment analysis into one single process; while at the same time the framework enables the implementation of different algorithms and approaches for semantic analysis

A Análise Crítica de Cinema e a Opinião do Público: Uma Comparação com Análise de Sentimentos

A crítica tem grande poder influenciador sobre os telespectadores, já que na maioria das vezes uma pessoa não pretende ir ao cinema para acabar se frustrando com o resultado final. O artigo apresenta uma comparação entre opinião da crítica especializada e opinião do público em relação aos filmes pré selecionados para análise. A metodologia de pesquisa descritiva foi adotada e, para construção dos resultados, foram extraídos dados de opinião do público através do Twitter e comparados à opiniões dos críticos presentes nos sites mais acessados no Brasil. A análise foi realizada pelo Google Cloud Natural Language, uma ferramenta que utiliza a análise de sentimentos. O resultado apresentou que pela análise de sentimentos, utilizando língua portuguesa, não foi possível garantir que existe diferença na opinião entre os dois grupos

Entwicklung eines SOA-basierten und anpassbaren Bewertungsdienstes für Inhalte aus sozialen Medien

Dieser Beitrag soll aufzeigen, wie ein anpassbarer Bewertungsdienst die Nutzung bürgergenerierter Inhalte aus sozialen Medien unterstützen kann. Dabei soll insbesondere geklärt werden, wie dieser gestaltet werden kann und wie Nutzer die Qualitätskriterien angemessen artikulieren können. Nach einer Darstellung von Grundlagen und verwandten Arbeiten wird anhand einer empirischen Vorstudie der Umgang von Behörden und Organisationen mit Sicherheitsaufgaben (BOS) mit bürgergenerierten Informationen betrachtet. Basierend auf den dort gewonnen Erkenntnissen wurde ein service-orientierter Bewertungsdienst entwickelt und in eine Anwendung integriert, welche so den Zugang zu bürgergenerierten Informationen aus verschiedenen sozialen Medien inklusive einer anpassbaren Qualitätsbewertung ermöglicht. Eine abschließende Evaluation illustriert deren mögliche Anwendung in der Praxis

Regional sentiment bias in social media reporting during crises

Crisis events such as terrorist attacks are extensively commented upon on social media platforms such as Twitter. For this reason, social media content posted during emergency events is increasingly being used by news media and in social studies to characterize the public’s reaction to those events. This is typically achieved by having journalists select ‘representative’ tweets to show, or a classifier trained on prior human-annotated tweets is used to provide a sentiment/emotion breakdown for the event. However, social media users, journalists and annotators do not exist in isolation, they each have their own context and world view. In this paper, we ask the question, ‘to what extent do local and international biases affect the sentiments expressed on social media and the way that social media content is interpreted by annotators’. In particular, we perform a multi-lingual study spanning two events and three languages. We show that there are marked disparities between the emotions expressed by users in different languages for an event. For instance, during the 2016 Paris attack, there was 16% more negative comments written in the English than written in French, even though the event originated on French soil. Furthermore, we observed that sentiment biases also affect annotators from those regions, which can negatively impact the accuracy of social media labelling efforts. This highlights the need to consider the sentiment biases of users in different countries, both when analysing events through the lens of social media, but also when using social media as a data source, and for training automatic classification models