Khan, A. and Baharudin, B. and Khan, K. (2010) Sentence based sentiment classification from online customer reviews. In: UNSPECIFIED.
Full text not available from this repository.Abstract
Sentiment analysis is the process of analyzing and classifying the rewires contents about a product, event, and place etc into positive, negative or neutral opinion. In this paper; we propose a sentence level machine learning approach for sentiment classification of online reviews. The proposed method extracts the subjective sentences from the reviews and label each sentence either positive or negative based on its word level feature using naïve Näve Bayesian (NB) classifier. The labeled sentences create an annotated set of sentences called as BOS (Bag-of-Sentences). We train Support Vector machine (SVM) classifier on the BOS for sentences polarity classification. The contextual information in each sentence structure is taken into consideration to calculate the semantic orientation. The effectiveness of the proposed method is evaluated thought simulation. Results show that our machine learning based proposed method on average achieves accuracy of 81 and 83 with some contextual information. This method improves the sentiment classification polarity on sentence level unlike the word level lexical feature based work, by focus on sentences, this also concentrate on contextual information. Copyright © 2010 ACM.
Item Type: | Conference or Workshop Item (UNSPECIFIED) |
---|---|
Additional Information: | cited By 16; Conference of 8th International Conference on Frontiers of Information Technology, FIT'10 ; Conference Date: 21 December 2010 Through 23 December 2010; Conference Code:84130 |
Uncontrolled Keywords: | Bayesian; Contextual information; Lexical features; Machine-learning; Online customers; Polarity classification; Semantic orientation; Sentence level; Sentence structures; Sentiment analysis; Sentiment classification; Word level, Feature extraction; Information technology; Learning systems; Semantics; Sodium, Classification (of information) |
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/971 |