Incorporating Word Significance into Aspect-Level Sentiment Analysis

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dc.contributor.author Mokhosi, Refuoe
dc.contributor.author Qin, ZhiGuang
dc.contributor.author Liu, Qiao
dc.contributor.author Shikali, Casper S.
dc.date.accessioned 2020-04-29T08:18:24Z
dc.date.available 2020-04-29T08:18:24Z
dc.date.issued 2019-08
dc.identifier.citation Applied Sciences; 9(17), 3522 en_US
dc.identifier.issn 2076-3417
dc.identifier.issn 2076-3417
dc.identifier.uri https://www.mdpi.com/2076-3417/9/17/3522/pdf
dc.identifier.uri http://repository.seku.ac.ke/handle/123456789/6032
dc.description DOI: 10.3390/app9173522 en_US
dc.description.abstract Aspect-level sentiment analysis has drawn growing attention in recent years, with higher performance achieved through the attention mechanism. Despite this, previous research does not consider some human psychological evidence relating to language interpretation. This results in attention being paid to less significant words especially when the aspect word is far from the relevant context word or when an important context word is found at the end of a long sentence. We design a novel model using word significance to direct attention towards the most significant words, with novelty decay and incremental interpretation factors working together as an alternative for position based models. The interpretation factor represents the maximization of the degree each new encountered word contributes to the sentiment polarity and a counter balancing stretched exponential novelty decay factor represents decaying human reaction as a sentence gets longer. Our findings support the hypothesis that the attention mechanism needs to be applied to the most significant words for sentiment interpretation and that novelty decay is applicable in aspect-level sentiment analysis with a decay factor β=0.7 . en_US
dc.language.iso en en_US
dc.publisher MDPI en_US
dc.subject aspect-level sentiment analysis en_US
dc.subject attention mechanism en_US
dc.subject novelty decay en_US
dc.subject incremental interpretation en_US
dc.subject stretched exponential law en_US
dc.title Incorporating Word Significance into Aspect-Level Sentiment Analysis en_US
dc.type Article en_US


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