Sentiment classification using sentence-level semantic orientation of opinion terms from blogs

Khan, A. and Baharudin, B. (2011) Sentiment classification using sentence-level semantic orientation of opinion terms from blogs. In: UNSPECIFIED.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Sentiment analysis is the procedure by which information is extracted from the opinions, appraisals and emotions of people in regards to entities, events and their attributes. In decision making, the opinions of others have a significant effect on customers ease in making choices regards to online shopping, choosing events, products, entities. In this paper, the rule based domain independent sentiment analysis method is proposed. The proposed method classifies subjective and objective sentences from reviews and blog comments. The semantic score of subjective sentences is extracted from SentiWordNet to calculate their polarity as positive, negative or neutral based on the contextual sentence structure. The results show the effectiveness of the proposed method and it outperforms the machine learning methods. The proposed method achieves an accuracy of 87 at the feedback level and 83 at the sentence level. © 2011 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 15; Conference of 3rd National Postgraduate Conference - Energy and Sustainability: Exploring the Innovative Minds, NPC 2011 ; Conference Date: 19 September 2011 Through 20 September 2011; Conference Code:88531
Uncontrolled Keywords: blog maining; Machine learning methods; Online shopping; Rule based; Semantic orientation; Sentence level; Sentence structures; sentiment analysis; Sentiment classification; Text mining, Data mining; Information retrieval; Learning systems; Semantics; Sustainable development; Text processing, Internet
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 09 Nov 2023 15:49
Last Modified: 09 Nov 2023 15:49
URI: https://khub.utp.edu.my/scholars/id/eprint/1594

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