Keeping in Time: Adding Temporal Context to Sentiment Analysis Models
Dean Ninalga
This paper presents a state-of-the-art solution to the LongEval CLEF 2023 Lab
Task 2: LongEval-Classification. The goal of this task is to improve and
preserve the performance of sentiment analysis models across shorter and longer
time periods. Our framework feeds date-prefixed textual inputs to a pre-trained
language model, where the timestamp is included in the text. We show
date-prefixed samples better conditions model outputs on the temporal context
of the respective texts. Moreover, we further boost performance by performing
self-labeling on unlabeled data to train a student model. We augment the
self-labeling process using a novel augmentation strategy leveraging the
date-prefixed formatting of our samples. We demonstrate concrete performance
gains on the LongEval-Classification evaluation set over non-augmented
self-labeling. Our framework achieves a 2nd place ranking with an overall score
of 0.6923 and reports the best Relative Performance Drop (RPD) of -0.0656 over
the short evaluation set.