Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Information Reliability on the Social Web - Models and Applications in Intelligent User Interfaces

The Social Web is undergoing continued evolution, changing the paradigm of information production, processing and sharing. Information sources have shifted from institutions to individual users, vastly increasing the amount of information available online. To overcome the information overload problem, modern filtering algorithms have enabled people to find relevant information in efficient ways. However, noisy, false and otherwise useless information remains a problem. We believe that the concept of information reliability needs to be considered along with information relevance to adapt filtering algorithms to today's Social Web. This approach helps to improve information search and discovery and can also improve user experience by communicating aspects of information reliability.This thesis first shows the results of a cross-disciplinary study into perceived reliability by reporting on a novel user experiment. This is followed by a discussion of modeling, validating, and communicating information reliability, including its various definitions across disciplines. A selection of important reliability attributes such as source credibility, competence, influence and timeliness are examined through different case studies. Results show that perceived reliability of information can vary greatly across contexts. Finally, recent studies on visual analytics, including algorithm explanations and interactive interfaces are discussed with respect to their impact on the perception of information reliability in a range of application domains

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

Multi Criteria Decision Making Approach For Product Aspect Extraction And Ranking In Aspect-Based Sentiment Analysis

Identifying product aspects in customer reviews can have a great influence on both business strategies as well as on customers’ decisions. Presently, most research focuses on machine learning, statistical, and Natural Language Processing (NLP) techniques to identify the product aspects in customer reviews. The challenge of this research is to formulate aspect identification as a decision-making problem. To this end, we propose a product aspect identification approach by combining multi-criteria decision-making (MCDM) with sentiment analysis. The suggested approach consists of two stages namely product aspect extraction and product aspect ranking