Spatial Sentiment Visualization for Text Data

Large numbers of online comments about a public consultation from citizens makes it difficult for decision-makers on public projects. To clarify various cognitions of participants is important. But reading through all comments, especially long ones, is unrealistic due to the high cost. An example dataset is 974 comments about whether residents support a new 0.5% Metro Vancouver Congestion Improvement Tax from PlaceSpeak. This brings out the need for text analysis. The intern puts forward two main methods to transform free-formed dataset to structured information with the help of visualization. This helps PlaceSpeak to form a workflow to study their text datasets in the future, and provide customers useful guidance for their decision-making process.

Faculty Supervisor:

Stan Matwin

Student:

Lulu Huang

Partner:

PlaceSpeak

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Dalhousie University