Property price prediction: a model utilising sentiment analysis

The increase in the use of social media has led many researchers and companies to investigate the potential uses of the data that is generated by these social media platforms. This research study investigates how the use of sentiment variables, obtained from the social media platform Twitter, can be used to augment housing transfer data in order to develop a predictive model. The Design Science Research (DSR) methodology was followed, guided by a Social Media Framework. Experimentation was required within the Design Cycle of the DSR methodology, which lead to the adoption of the Experimental Research methodology within this cycle. An initial literature review identified regression models for property price prediction. Through experimentation, Gradient Boosting regression was identified as an optimal regression model for this purpose. Thereafter a review of sentiment analysis models was conducted which resulted in the proposal of a CNN-LSTM model for the classification of Tweets. Initial experimentation conducted with this proposed model resulted in an obtained accuracy comparable to the top performing sentiment analysis models identified. A dataset obtained through SemEval, a series of evaluations of computational semantic analysis systems, was used for this phase. For the final experimentation, The CNN-LSTM model was used to obtain sentiment variables from Tweets that were collected from the Western Cape Province in 2017. This property dataset was augmented with the sentiment variables, after which experimentation was conducted by applying Gradient Boosting regression. The augmentation was done in two ways, either based on suburb pertaining to the property, or to the month in which the property was transferred. The results indicate that a model for Property Price Prediction Utilising Sentiment Analysis demonstrates a small improvement when suburb-based sentiment, obtained from Tweets with a minimum threshold per suburb, is utilised. An important finding was the fact that, when geo-coordinates are removed from the dataset, the sentiment variables replace them in the regression results, producing the same level as accuracy as when the coordinates are included