Resumen
With the vast amount of social media posts available online, topic modeling and sentiment analysis have become central methods to better understand and analyze online behavior and opinion. However, semantic and sentiment analysis have rarely been combined for joint topic-sentiment modeling which yields semantic topics associated with sentiments. Recent breakthroughs in natural language processing have also not been leveraged for joint topic-sentiment modeling so far. Inspired by these advancements, this paper presents a novel framework for joint topic-sentiment modeling of short texts based on pre-trained language models and a clustering approach. The method leverages techniques from dimensionality reduction and clustering for which multiple algorithms were considered. All configurations were experimentally compared against existing joint topic-sentiment models and an independent sequential baseline. Our framework produced clusters with semantic topic quality scores of up to 0.23
0.23
while the best score among the previous approaches was 0.12
0.12
. The sentiment classification accuracy increased from 0.35
0.35
to 0.72
0.72
and the uniformity of sentiments within the clusters reached up to 0.9
0.9
in contrast to the baseline of 0.56
0.56
. The presented approach can benefit various research areas such as disaster management where sentiments associated with topics can provide practical useful information.