Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence Structures
Product information has been propagated online via forums and social media. Lots of merchandise are recommended via an expert system method and is considered for purchase by online comments or product reviews. For predicting people’s opinions on products, studying people’s thoughts via extracting in...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/10/24/3187 |
| _version_ | 1797505170208194560 |
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| author | Jaechoon Jo Gyeongmin Kim Kinam Park |
| author_facet | Jaechoon Jo Gyeongmin Kim Kinam Park |
| author_sort | Jaechoon Jo |
| collection | DOAJ |
| description | Product information has been propagated online via forums and social media. Lots of merchandise are recommended via an expert system method and is considered for purchase by online comments or product reviews. For predicting people’s opinions on products, studying people’s thoughts via extracting information in documents is referred to as sentiment analysis. Finding sentiment-target word pairs is an important sentiment mining research issue. With the Korean language, as the predicate appears at the very end, it is not easy to find the exact word pairs without first identifying the syntactic structure of the sentence. In this study, we propose a model that parses sentence structures and extracts sentiment-target word pairs from the parse tree. The proposed model extracts the sentiment-target word pairs that appear in the sentence by using parsing and statistical methods. For extracting sentiment-target word pairs, this model uses a sentiment word extractor and a target word extractor. After testing data from 4000 movie reviews, the applicable model showed high performance in both accuracy 93.25 (+14.45) and F1-score 82.29 (+3.31) compared with others. However, improvements in the recall rate (−0.35) are needed and computational costs must be reduced. |
| first_indexed | 2024-03-10T04:14:47Z |
| format | Article |
| id | doaj.art-2771989810494f1fa49afc1633b7b4e2 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T04:14:47Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-2771989810494f1fa49afc1633b7b4e22023-11-23T08:03:20ZengMDPI AGElectronics2079-92922021-12-011024318710.3390/electronics10243187Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence StructuresJaechoon Jo0Gyeongmin Kim1Kinam Park2Division of Computer Engineering, Hanshin University, Osan 18101, KoreaDepartment of Computer Science and Engineering, Korea University, Seoul 02841, KoreaDepartment of Computer Science and Engineering, Korea University, Seoul 02841, KoreaProduct information has been propagated online via forums and social media. Lots of merchandise are recommended via an expert system method and is considered for purchase by online comments or product reviews. For predicting people’s opinions on products, studying people’s thoughts via extracting information in documents is referred to as sentiment analysis. Finding sentiment-target word pairs is an important sentiment mining research issue. With the Korean language, as the predicate appears at the very end, it is not easy to find the exact word pairs without first identifying the syntactic structure of the sentence. In this study, we propose a model that parses sentence structures and extracts sentiment-target word pairs from the parse tree. The proposed model extracts the sentiment-target word pairs that appear in the sentence by using parsing and statistical methods. For extracting sentiment-target word pairs, this model uses a sentiment word extractor and a target word extractor. After testing data from 4000 movie reviews, the applicable model showed high performance in both accuracy 93.25 (+14.45) and F1-score 82.29 (+3.31) compared with others. However, improvements in the recall rate (−0.35) are needed and computational costs must be reduced.https://www.mdpi.com/2079-9292/10/24/3187opinion miningparserword extractiondata miningstatistical model |
| spellingShingle | Jaechoon Jo Gyeongmin Kim Kinam Park Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence Structures Electronics opinion mining parser word extraction data mining statistical model |
| title | Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence Structures |
| title_full | Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence Structures |
| title_fullStr | Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence Structures |
| title_full_unstemmed | Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence Structures |
| title_short | Sentiment-Target Word Pair Extraction Model Using Statistical Analysis of Sentence Structures |
| title_sort | sentiment target word pair extraction model using statistical analysis of sentence structures |
| topic | opinion mining parser word extraction data mining statistical model |
| url | https://www.mdpi.com/2079-9292/10/24/3187 |
| work_keys_str_mv | AT jaechoonjo sentimenttargetwordpairextractionmodelusingstatisticalanalysisofsentencestructures AT gyeongminkim sentimenttargetwordpairextractionmodelusingstatisticalanalysisofsentencestructures AT kinampark sentimenttargetwordpairextractionmodelusingstatisticalanalysisofsentencestructures |