Improving Tweet Representations using Temporal and User Context
Table 1: User profile attribute classification - F1 Score
['[BOLD] Algorithm', '[BOLD] Spouse', '[BOLD] Education', '[BOLD] Job']
HDV’s assumption of giving equal attention value to the temporal context also results in lower accuracy compared with our models. SD model outperforms HDV in two tasks, which substantiates our claim against HDV’s naïve assumption for social media. Our model with user vector outperforming the baselines for Education and Job attribute classification, shows the need to consider the user characteristics while modelling his/her tweets. The poor results for Spouse task suggest that this dataset has too many topic shifts and that the user vector turned out to be less accurate. We observe that in some cases HDV outperforms the SD model, mainly due to the inability of the SD model to utilize the context information from farther tweets which are relevant with respect to the target tweet. Our models are 19.66%, 2.27% and 2.22% better compared to the baselines for the spouse, education and job attributes respectively.