Predicting sentiment about places of living

Abstract

Nowadays studies about the quality of life in major cities are often published in the daily news. These contain ranked list according to the quality of living with indicators representing various aspects. Typical indicators are crime level, transport, health care etc. Along with the flourishing of different social medias, a huge amount of information could be collected from the Internet. Moreover, machine learning as a branch of artificial intelligence becomes more and more prominent. The recent advances in machine learning had found usage in a wide range of applications. One of such application is that of text categorization and sentiment analysis. Relying on these conditions, this thesis aims to create a classifier to predict the sentiment about places of living. In this thesis a ranking list of cities of Mercer is taken use. As a result of the quality of living survey 230 cities of the world are ranked in the list. Text form information of microblogging is chosen as our testbed. Specifically, tweets, microblogging messages from the popular website Twitter, are studied. The tweets chosen for this study are those about cities living standard and contain rich sentiment information. Classification label is assigned to cities under study by their position in the ranking list. After sentiment related features are extracted, machine learning techniques are then applied on the collected tweets. As a result, a classifier with a strong baseline for predicting sentiment about places of living is trained using logistic regression model.