Mubashar Hussain¹, Toqir A. Rana^2,3, Aksam Iftikhar⁴, M. Usman Ashraf^5,*, Muhammad Waseem Iqbal⁶, Ahmed Alshaflut⁷, Abdullah Alourani⁸

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4641-4656, 2022, DOI:10.32604/cmc.2022.024759

Abstract In the field of sentiment analysis, extracting aspects or opinion targets from user reviews about a product is a key task. Extracting the polarity of an opinion is much more useful if we also know the targeted Aspect or Feature. Rule based approaches, like dependency-based rules, are quite popular and effective for this purpose. However, they are heavily dependent on the authenticity of the employed parts-of-speech (POS) tagger and dependency parser. Another popular rule based approach is to use sequential rules, wherein the rules formulated by learning from the user’s behavior. However, in general, the sequential rule-based approaches have poor… More >

Jibran Mir¹, Azhar Mahmood^2,*, Shaheen Khatoon³

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3491-3509, 2022, DOI:10.32604/cmc.2022.019952

Abstract Aspect's extraction is a critical task in aspect-based sentiment analysis, including explicit and implicit aspects identification. While extensive research has identified explicit aspects, little effort has been put forward on implicit aspects extraction due to the complexity of the problem. Moreover, existing research on implicit aspect identification is widely carried out on product reviews targeting specific aspects while neglecting sentences’ dependency problems. Therefore, in this paper, a multi-level knowledge engineering approach for identifying implicit movie aspects is proposed. The proposed method first identifies explicit aspects using a variant of BiLSTM and CRF (Bidirectional Long Short Memory-Conditional Random Field), which serve… More >