NOVEL MULTI-LEVEL ASPECT BASED SENTIMENT ANALYSIS FOR IMPROVED ROOT-CAUSE ANALYSIS

Autor:	Naveenkumar Seerangan, Vijayaragavan Shanmugam
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	root cause analysis sentiment identification aspect extraction machine learning Computer engineering. Computer hardware TK7885-7895
Zdroj:	ICTACT Journal on Soft Computing, Vol 11, Iss 3, Pp 2384-2389 (2021)
Druh dokumentu:	article
ISSN:	0976-6561 2229-6956
DOI:	10.21917/ijsc.2021.0340
Popis:	Aspect extraction and sentiment identification are the two important tasks to provide effective root cause analysis. This work presents a Multi-Level Aspect based Sentiment Analysis (MLASA) model that integrates the aspect extraction and sentiment identification modules to provide effective root cause analysis. The aspect extraction module performs token filtration, followed by rule based aspect identification. The heterogeneous multi-level sentiment identification phase performs aspect based sentiment identification. First level performs magnitude along and polarity identification of text, while the second level performs polarity identification using multiple machine learning models. The results are aggregated and ranked based on aspect significance and sentiment magnitude. Experiments and comparisons show effective performance of the MLASA model.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/d1c05896557f49999819f1733f6486ac Zobrazit plný text záznamu View record in DOAJ