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

Tanzania: Pilot Rural Investment Climate Assessment. Stimulating Nonfarm Microenterprise Growth

Tanzania’s Pilot Rural Investment Climate Assessment (RICA) measures the economic environment of non-farm entrepreneurs. The pilot assessment has three key objectives: it aims to better understand the rural non-farm economy in Tanzania, shed light on rural enterprise dynamics and business constraints, and reflect on areas where government policies are readily directed to help promote rural non-farm enterprise activity. The RICA is based on an analysis of a unique survey data set collected by the National Bureau of Statistics (NBS) during January and March 2005, covering enterprises, households, and communities in all seven geographical zones of the country. Selected findings are: (i) Non-farm activities are an important source of income for approximately 1.4 million rural households, (ii) Tanzanian rural non-farm enterprises differ from their urban counterparts, (iii) the predominant entrepreneurial activity is trading, (iv) labor productivity is typically low, (v) formal enterprises have higher levels of labor productivity than informal, (vi) the rate of new firm creation appears to be lower than in other African countries, and (vii) only a minority of enterprises propels employment growth. The pilot approach call for a careful evaluation of the following recommendations, which presented to stimulate dialogue and future analysis: (i) favorable policies and investments for agriculture play a big role for rural enterprises, (ii) maintaining favorable internal trade policies may therefore be of utmost importance in determining enterprise performance, (iii) microcredit and savings may offer a tool for promoting rural non-farm activity in buoyant rural markets, (iv) easing bottlenecks in rural infrastructure is important, (v) exploring options for better telecommunications via private sector cell phone nodes may be an attractive policy option to stimulate entrepreneurial activities, (vi) continuation of business registration reform and effective implementation at the local level remains a high priority, and (vii) future analysis should address knowledge gaps.Tanzania; rural labor markets; enterprise performance; informal sector