Abstract:
Financial solvency analysis of a geographic area is a very challenging task because it costs lots of money and a big volume of data handling is involved. Every country in the world checks its ftnancial status, which is known as the economic census. There are some techniques to measure ftnancial status like using of some questionnaires where data is collected from fteld level manually, or using ftnancial statements. These techniques are old and these use data collection manually which increase the cost, money and effort. The use of social and web media network has been increased a lot. As a part of socio-activities people share their personal information like occupation, home town, events, plan with his/her friends on social media. On the other hand, web media or online access of information provide information for economic establishments (e.g. public institutions, private companies, etc). There are many research work like cultural boundary detection, community detection, food consumption statistics, sentiment analysis, and personality predicting, have been done using social media. It is very important to have economic census for a country. In this study, an automated economic zone ftnding framework has been designed using social-sensing and web mining. Here, data has been collected using social and web media. By using the proposed framework, the ftnancial status of eight major divisional cities of Bangladesh has been successfully detected. And the last of all, the city's ranks have been measured according to their ftnancial solvency. This technique is automated, so it requires less time and it costs less money than other traditional surveyed methods. According to best of our knowledge, this is the ftrst technique in our country that uses the web and social media to verify the ftnancial status of different cities and this technique performs better than existing surveyed approaches.
