Sentiment Classification for Hotel Booking Review Based on Sentence Dependency Structure and Sub-Opinion Analysis

Tran Sy BANG; Virach SORNLERTLAMVANICH

doi:10.1587/transinf.2016IIP0038

Sentiment Classification for Hotel Booking Review Based on Sentence Dependency Structure and Sub-Opinion Analysis

Tran Sy BANG, Virach SORNLERTLAMVANICH

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This paper presents a supervised method to classify a document at the sub-sentence level. Traditionally, sentiment analysis often classifies sentence polarity based on word features, syllable features, or N-gram features. A sentence, as a whole, may contain several phrases and words which carry their own specific sentiment. However, classifying a sentence based on phrases and words can sometimes be incoherent because they are ungrammatically formed. In order to overcome this problem, we need to arrange words and phrase in a dependency form to capture their semantic scope of sentiment. Thus, we transform a sentence into a dependency tree structure. A dependency tree is composed of subtrees, and each subtree allocates words and syllables in a grammatical order. Moreover, a sentence dependency tree structure can mitigate word sense ambiguity or solve the inherent polysemy of words by determining their word sense. In our experiment, we provide the details of the proposed subtree polarity classification for sub-opinion analysis. To conclude our discussion, we also elaborate on the effectiveness of the analysis result.

Publication: IEICE TRANSACTIONS on Information Vol.E101-D No.4 pp.909-916

Publication Date: 2018/04/01

Publicized: 2018/01/19

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2016IIP0038

Type of Manuscript: Special Section PAPER (Special Section on Intelligent Information and Communication Technology and its Applications to Creative Activity Support)

Category: Datamining Technologies

Authors

Tran Sy BANG
Thammasat University
Virach SORNLERTLAMVANICH
Thammasat University

Keyword

sentiment analysis, sentence dependency parsing, subtree opinions, Vietnamese sentiment classification, hotel review classification

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Tran Sy BANG, Virach SORNLERTLAMVANICH, "Sentiment Classification for Hotel Booking Review Based on Sentence Dependency Structure and Sub-Opinion Analysis" in IEICE TRANSACTIONS on Information, vol. E101-D, no. 4, pp. 909-916, April 2018, doi: 10.1587/transinf.2016IIP0038.
Abstract: This paper presents a supervised method to classify a document at the sub-sentence level. Traditionally, sentiment analysis often classifies sentence polarity based on word features, syllable features, or N-gram features. A sentence, as a whole, may contain several phrases and words which carry their own specific sentiment. However, classifying a sentence based on phrases and words can sometimes be incoherent because they are ungrammatically formed. In order to overcome this problem, we need to arrange words and phrase in a dependency form to capture their semantic scope of sentiment. Thus, we transform a sentence into a dependency tree structure. A dependency tree is composed of subtrees, and each subtree allocates words and syllables in a grammatical order. Moreover, a sentence dependency tree structure can mitigate word sense ambiguity or solve the inherent polysemy of words by determining their word sense. In our experiment, we provide the details of the proposed subtree polarity classification for sub-opinion analysis. To conclude our discussion, we also elaborate on the effectiveness of the analysis result.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016IIP0038/_p

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@ARTICLE{e101-d_4_909,
author={Tran Sy BANG, Virach SORNLERTLAMVANICH, },
journal={IEICE TRANSACTIONS on Information},
title={Sentiment Classification for Hotel Booking Review Based on Sentence Dependency Structure and Sub-Opinion Analysis},
year={2018},
volume={E101-D},
number={4},
pages={909-916},
abstract={This paper presents a supervised method to classify a document at the sub-sentence level. Traditionally, sentiment analysis often classifies sentence polarity based on word features, syllable features, or N-gram features. A sentence, as a whole, may contain several phrases and words which carry their own specific sentiment. However, classifying a sentence based on phrases and words can sometimes be incoherent because they are ungrammatically formed. In order to overcome this problem, we need to arrange words and phrase in a dependency form to capture their semantic scope of sentiment. Thus, we transform a sentence into a dependency tree structure. A dependency tree is composed of subtrees, and each subtree allocates words and syllables in a grammatical order. Moreover, a sentence dependency tree structure can mitigate word sense ambiguity or solve the inherent polysemy of words by determining their word sense. In our experiment, we provide the details of the proposed subtree polarity classification for sub-opinion analysis. To conclude our discussion, we also elaborate on the effectiveness of the analysis result.},
keywords={},
doi={10.1587/transinf.2016IIP0038},
ISSN={1745-1361},
month={April},}

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TY - JOUR
TI - Sentiment Classification for Hotel Booking Review Based on Sentence Dependency Structure and Sub-Opinion Analysis
T2 - IEICE TRANSACTIONS on Information
SP - 909
EP - 916
AU - Tran Sy BANG
AU - Virach SORNLERTLAMVANICH
PY - 2018
DO - 10.1587/transinf.2016IIP0038
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2018
AB - This paper presents a supervised method to classify a document at the sub-sentence level. Traditionally, sentiment analysis often classifies sentence polarity based on word features, syllable features, or N-gram features. A sentence, as a whole, may contain several phrases and words which carry their own specific sentiment. However, classifying a sentence based on phrases and words can sometimes be incoherent because they are ungrammatically formed. In order to overcome this problem, we need to arrange words and phrase in a dependency form to capture their semantic scope of sentiment. Thus, we transform a sentence into a dependency tree structure. A dependency tree is composed of subtrees, and each subtree allocates words and syllables in a grammatical order. Moreover, a sentence dependency tree structure can mitigate word sense ambiguity or solve the inherent polysemy of words by determining their word sense. In our experiment, we provide the details of the proposed subtree polarity classification for sub-opinion analysis. To conclude our discussion, we also elaborate on the effectiveness of the analysis result.
ER -