Aspect Extraction in Domain Lexicon Generation: A New Frequency-Based Approach

被引：0

作者：

Zayet, Tasnim M. A. ^{[1
]}

Ismail, Maizatul Akmar ^{[1
]}

Varathan, Kasturi Dewi ^{[1
]}

机构：

[1] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Informat Syst, Kuala Lumpur 50603, Malaysia

来源：

IEEE ACCESS | 2024年 / 12卷

关键词：

Feature extraction; Data mining; Frequency-domain analysis; Social networking (online); Sentiment analysis; Semantics; Accuracy; Statistical analysis; Text processing; Text mining; Aspect; domain lexicon; frequency-based; sentiment analysis; statistical; word extraction; context; FEATURE-SELECTION; SENTIMENT;

D O I：

10.1109/ACCESS.2024.3442930

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Domain sentimental lexicon building become an attractive field in recent years. This is due to the increased number of users' generated data through the internet besides the different sentiments of opinion words in different contexts. Domain lexicons mainly consist of opinion pairs and their associated sentiment. Any opinion pair is formed by a domain word and one of its associated opinion words. Therefore, to generate a domain lexicon from a domain corpus, domain word extraction is needed with their associated opinion words. One of the traditional approaches is frequency-based approaches. However, the ambiguity problem is a big concern of these approaches. This paper introduced a frequency-based equation that considers the context of the words for domain word extraction. The equation was tested on five Amazon reviews datasets and it proved its efficiency over other used frequency-based equations in terms of recall and precision. Therefore, more related lexicons to the domains were generated.

引用

页码：138972 / 138984

页数：13

共 49 条

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