VISUALIZAÇÃO DE INFORMAÇÃO DE OPINIÕES ONLINE SOBRE RESTAURANTES: USO DE TÉCNICAS ORIENTADAS À VISUALIZAÇÃO DE GRAFOS

The scientific interest in the analysis of online reviews available on the Web 2.0 is growing. The results are usually massive and made available in tabular or textual format. As a result, the readability of information is reduced and consequently the human-machine interaction is compromised. Moreover, the area of Information Visualization (VI) can be a valuable help to remedy this deficiency, but has been little explored in this context. This article introduces a novel application of techniques of graph-oriented visualization from the data analysis of online reviews about restaurants. Special emphasis is given to the visual mapping of the main aspects mentioned (e.g., the quality of food or service) as well as the type of attitude expressed in the opinion (e.g., appreciation or judgment). Views are designed to support the decision-making process of restauranteurs, customers and prospective customers.Existe atualmente um crescente interesse científico na análise de opiniões online disponíveis na Web 2.0. Os resultados são normalmente volumosos e disponibilizados em formato tabular ou textual. Em função disso, a legibilidade da informação é reduzida e consequentemente a interação homem-máquina fica comprometida. Por outro lado, a área de Visualização de Informação (VI) pode ser uma valiosa ajuda para colmatar essa deficiência, mas tem sido muito pouco explorada nesse âmbito. Este artigo introduz a aplicação inovadora de técnicas orientadas à visualização de grafos a dados provenientes da análise de opiniões online sobre restaurantes. Especial ênfase é dada ao mapeamento visual dos principais aspectos mencionados (por exemplo, a qualidade da comida ou o serviço prestado) bem como o tipo de atitude expresso na opinião (por exemplo, apreciação ou julgamento). As visualizações visam apoiar o processo de tomada de decisão tanto de gestores de restaurantes como de clientes e futuros clientes

Analysis of the Geosocial Landscape in the City of Toronto

Microblogging on geosocial platforms is a popular form of online communication where users post information about their daily lives and challenges. Since the launch of Twitter in 2006, information sharing through social media has become a largely unused data repository. Tweets often convey content about the users sentiment as it is happening. As such, Tweets can be viewed as a proxy of public mood. In this thesis, I performed a sentiment analysis of all public geo-located Tweets posted by a variety of Twitter users between September 2013 and October 2014. Each Tweet was processed through a custom algorithm to extract 8 different emotions: Anger, Confusion, Disgust, Fear, Happiness, Sadness, Shame, and Surprise. I then created an emotional landscape to display variance in emotion across the city of Toronto. The emotional landscape presented interesting emotional polarity change between the core and the periphery of the city. Neighbourhood profiles were then used to compare the emotional differences resource access could individual’s ability to cope and mediate stress. I found that individuals living within close proximity to greenspace expressed increased levels of positivity though they have decreased access to built resources. I also found that individuals within Neighbourhood Improvement Areas experienced an increased risk of negativity. I believe large-scale analyses of public sentiment can provide valuable information for further analysis of resource use in an effort to reduce negative health effects long term

Investigating transportation research based on social media analysis: A systematic mapping review

Social media is a pool of users’ thoughts, opinions, surrounding environment, situation and others. This pool can be used as a real-time and feedback data source for many domains such as transportation. It can be used to get instant feedback from commuters; their opinions toward the transportation network and their complaints, in addition to the traffic situation, road conditions, events detection and many others. The problem is in how to utilize social media data to achieve one or more of these targets. A systematic review was conducted in the field of transportation-related research based on social media analysis (TRRSMA) from the years between 2008 and 2018; 74 papers were identified from an initial set of 703 papers extracted from 4 digital libraries. This review will structure the field and give an overview based on the following grounds: activity, keywords, approaches, social media data and platforms and focus of the researches. It will show the trend in the research subjects by countries, in addition to the activity trends, platforms usage trend and others. Further analysis of the most employed approach (Lexicons) and data (text) will be also shown. Finally, challenges and future works are drawn and proposed

What tweets and retweets on twitter can tell for the restaurant industry: A big-data approach

In the Internet age, the sheer volume of information can be generated and disseminated through online user-generated content (UGC). Within the context of Twitter, the retweeting function is one of the key mechanisms, which enables the information diffusion process among users in the social network. Stimulated by this concern, the purpose of the current study was to investigate the effects of textual content including the sentiments, emotions, and language style matching (LSM) of Twitter, a series of statistical analyses are conducted to the Twitter dataset with around one million pieces of customer tweet information. The results indicated that sentiments, emotions, and LSM have significant influences on customer retweeting behavior. Besides, significant differences were identified of both sentiments and emotions based on both six periods of the timeline analysis and the geographic distance at the city level, state level, and nationwide level. Discussions and implications interpreted the significance of the most valuable findings and suggested some important insights to both academia and industry