  Sentiment Analysis (SA) is a crucial aspect of Natural Language Processing
(NLP), addressing subjective assessments in textual content. Syntactic parsing
is useful in SA because explicit syntactic information can improve accuracy
while providing explainability, but it tends to be a computational bottleneck
in practice due to the slowness of parsing algorithms. This paper addresses
said bottleneck by using a SEquence Labeling Syntactic Parser (SELSP) to inject
syntax into SA. By treating dependency parsing as a sequence labeling problem,
we greatly enhance the speed of syntax-based SA. SELSP is trained and evaluated
on a ternary polarity classification task, demonstrating its faster performance
and better accuracy in polarity prediction tasks compared to conventional
parsers like Stanza and to heuristic approaches that use shallow syntactic
rules for SA like VADER. This increased speed and improved accuracy make SELSP
particularly appealing to SA practitioners in both research and industry. In
addition, we test several sentiment dictionaries on our SELSP to see which one
improves the performance in polarity prediction tasks. Moreover, we compare the
SELSP with Transformer-based models trained on a 5-label classification task.
The results show that dictionaries that capture polarity judgment variation
provide better results than dictionaries that ignore polarity judgment
variation. Moreover, we show that SELSP is considerably faster than
Transformer-based models in polarity prediction tasks.
