Aspect-based　Sentiment　Analysis　(ABSA)　is　a　crucial　natural　language　understanding　(NLU)　research　field　which　aims　to　accurately　recognize　reviewers＇　opinions　on　different　aspects　of　products　and　services.　Despite　the　prominence　of　recent　ABSA　applications,　mainstream　ABSA　approaches　inevitably　rely　on　large-scale　supervised　corpora,　and　their　final　performances　is　susceptible　to　the　quality　of　the　training　datasets.　However,　annotating　sufficient　data　is　labour　intensive,　which　presents　a　significant　barrier　for　generalizing　a　high-quality　sentiment　analysis　model.　Nonetheless,　humans　can　make　more　accurate　judgement　based　on　their　external　background　knowledge,　such　as　factoid　triples　knowledge　and　event　causality.　Inspired　by　the　investigations　on　external　knowledge　enhancement　strategies　in　other　popular　NLP　research,　we　propose　a　novel　knowledge　augmentation　framework　for　ABSA,　named　the　Oxford　Dictionary　descriptive　knowledge-infused　aspect-based　sentiment　analysis　(DictABSA).　Comprehensive　experiments　with　many　state-of-the-art　approaches　on　several　widely　used　benchmarks　demonstrate　that　our　proposed　DictABSA　significantly　outperforms　previous　main-stream　ABSA　methods.　For　instance,　compared　with　the　baselines,　our　BERT-based　knowledge　infusion　strategy　achieves　a　substantial　6.42%　and　5.26%　absolute　accuracy　gain　when　adopting　BERT-SPC　on　SemEval2014　and　ABSA-DeBERTa　on　ACLShortData,　respectively.　Furthermore,　to　effectively　make　use　of　dictionary　knowledge　we　devise　several　alternative　knowledge　infusion　strategies.　Extensive　experiments　using　different　knowledge　infused　strategies　further　demonstrate　that　the　proposed　knowledge　infusion　strategies　effectively　enhance　the　sentiment　polarity　identification　capability.　The　Python　implementation　of　our　DictABSA　is　publicly　available　at　https://github.com/albert-jin/DictionaryFused-E2E-ABSA.