深度学习算法在藏文情感分析中的应用研究
TP391.1; 针对以往进行藏文情感分析时算法忽略藏文语句结构、词序等重要信息而导致结果准确率较低的问题,将深度学习领域内的递归自编码算法引入藏文情感分析中,以更深层次提取语义情感信息.将藏文分词后,用词向量表示词语,则藏文语句变为由词向量组成的矩阵;利用无监督递归自编码算法对该矩阵向量化,此时获得的最佳藏文语句向量编码融合了语义、语序等重要信息;利用藏文语句向量和其对应的情感标签,有监督地训练输出层分类器以预测藏文语句的情感倾向.在实例验证部分,探讨了不同向量维度、重构误差系数及语料库大小对算法准确度的影响,并分析了语料库大小和模型训练时间之间的关系,指出若要快速完成模型的训练,可适当减小...
Saved in:
| Published in | 计算机科学与探索 Vol. 11; no. 7; pp. 1122 - 1130 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
西藏大学 藏文信息技术研究中心,拉萨,850000%西南交通大学 信息科学与技术学院,成都,610031%西藏大学 藏文信息技术研究中心,拉萨 850000
2017
西南交通大学 信息科学与技术学院,成都 610031 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1673-9418 |
| DOI | 10.3778/j.issn.1673-9418.1611062 |
Cover
| Abstract | TP391.1; 针对以往进行藏文情感分析时算法忽略藏文语句结构、词序等重要信息而导致结果准确率较低的问题,将深度学习领域内的递归自编码算法引入藏文情感分析中,以更深层次提取语义情感信息.将藏文分词后,用词向量表示词语,则藏文语句变为由词向量组成的矩阵;利用无监督递归自编码算法对该矩阵向量化,此时获得的最佳藏文语句向量编码融合了语义、语序等重要信息;利用藏文语句向量和其对应的情感标签,有监督地训练输出层分类器以预测藏文语句的情感倾向.在实例验证部分,探讨了不同向量维度、重构误差系数及语料库大小对算法准确度的影响,并分析了语料库大小和模型训练时间之间的关系,指出若要快速完成模型的训练,可适当减小数据集语句条数.实例验证表明,在最佳参数组合下,所提算法准确度比传统机器学习算法中性能较好的语义空间模型高约8.6%. |
|---|---|
| AbstractList | TP391.1; 针对以往进行藏文情感分析时算法忽略藏文语句结构、词序等重要信息而导致结果准确率较低的问题,将深度学习领域内的递归自编码算法引入藏文情感分析中,以更深层次提取语义情感信息.将藏文分词后,用词向量表示词语,则藏文语句变为由词向量组成的矩阵;利用无监督递归自编码算法对该矩阵向量化,此时获得的最佳藏文语句向量编码融合了语义、语序等重要信息;利用藏文语句向量和其对应的情感标签,有监督地训练输出层分类器以预测藏文语句的情感倾向.在实例验证部分,探讨了不同向量维度、重构误差系数及语料库大小对算法准确度的影响,并分析了语料库大小和模型训练时间之间的关系,指出若要快速完成模型的训练,可适当减小数据集语句条数.实例验证表明,在最佳参数组合下,所提算法准确度比传统机器学习算法中性能较好的语义空间模型高约8.6%. |
| Abstract_FL | During Tibetan sentiment analysis in past, the algorithm always ignores some important information like sentences structure and words order etc, which lead low accuracy of sentiment analysis. To deeply get more senti-ment details, this paper proposes a novel approach of Tibetan sentiment analysis based on deep learning. Firstly, one word in Tibetan is represented by a word vector while one sentence is represented by a matrix which is composed by its word vectors;Secondly, the matrix is turned into a vector which contains most important details such as sentence meaning and words order etc, through an unsupervised recursive auto encoder algorithm; Finally, the classifier in output layer is trained by supervised method which uses the word vectors and its sentiment tags. In the experiment part, this paper discusses the selection of word vector dimensions and reconstruction error weights, studies corpus amount how to affect algorithm accuracy, and analyzes the relation between corpus amount and training time. The experimental results demonstrate that the proposed method can improve accuracy up 8.6%compared with semantic space model which is almost the best in traditional machine learning algorithm. |
| Author | 刘月 普次仁 翟东海 侯佳林 |
| AuthorAffiliation | 西藏大学 藏文信息技术研究中心,拉萨,850000%西南交通大学 信息科学与技术学院,成都,610031%西藏大学 藏文信息技术研究中心,拉萨 850000;西南交通大学 信息科学与技术学院,成都 610031 |
| AuthorAffiliation_xml | – name: 西藏大学 藏文信息技术研究中心,拉萨,850000%西南交通大学 信息科学与技术学院,成都,610031%西藏大学 藏文信息技术研究中心,拉萨 850000;西南交通大学 信息科学与技术学院,成都 610031 |
| Author_FL | LIU Yue HOU Jialin PU Ciren ZHAI Donghai |
| Author_FL_xml | – sequence: 1 fullname: PU Ciren – sequence: 2 fullname: HOU Jialin – sequence: 3 fullname: LIU Yue – sequence: 4 fullname: ZHAI Donghai |
| Author_xml | – sequence: 1 fullname: 普次仁 – sequence: 2 fullname: 侯佳林 – sequence: 3 fullname: 刘月 – sequence: 4 fullname: 翟东海 |
| BookMark | eNo9jT1LAzEchzNUsNZ-B1eHO_NPcsndKMU3KLh0L5fkIj0lBaOos_Vl0-FqQUQ7lTpUEMGXfp7enR_DguL0e3iG57eEKrZrE4RWAPtUiHAt9TvOWR-4oF7EIJwTAOakgqr_bhHVnetIHDBGQPCwiqLi4zWfjvLJaPY1LF8GxVs_fxh_D26Ku6vi_KLoPeXXl8Xj7exzUt738mlWZuNymJXP78towcQHLqn_bQ21NjdajW2vubu101hveooT8IQyIBPGtGaJUJxTUIZgY3SstMJE0diQMEgiwwFLjZnAhEopowh0YBiPaQ2t_mZPYmtiu9dOu8eHdn7YTl26f3p25AgGgQUGoD8B7WJY |
| ClassificationCodes | TP391.1 |
| ContentType | Journal Article |
| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.3778/j.issn.1673-9418.1611062 |
| DatabaseName | Wanfang Data Journals - Hong Kong WANFANG Data Centre Wanfang Data Journals 万方数据期刊 - 香港版 China Online Journals (COJ) China Online Journals (COJ) |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| DocumentTitle_FL | Deep Learning Algorithm Applied in Tibetan Sentiment Analysis |
| EndPage | 1130 |
| ExternalDocumentID | jsjkxyts201707011 |
| GrantInformation_xml | – fundername: The National Natural Science Foundation of China under Grant No. 61540060; the National Soft Science Re-search Program of China under Grant No. 2013GXS4D150; the Research Program of Science and Tech-nology Department of Tibet Autonomous Region |
| GroupedDBID | 2B. 4A8 92I 93N ALMA_UNASSIGNED_HOLDINGS M~E PSX TCJ |
| ID | FETCH-LOGICAL-c621-7cf1be44dd4e7c6631cf20ffdacdc02c3af285e9f610bd047023bbb991d5f46a3 |
| ISSN | 1673-9418 |
| IngestDate | Thu May 29 04:00:17 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 7 |
| Keywords | 情感分析 deep learning recursive auto encoder sentiment analysis recursive neural networks 深度学习 递归自编码 递归神经网络 |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c621-7cf1be44dd4e7c6631cf20ffdacdc02c3af285e9f610bd047023bbb991d5f46a3 |
| PageCount | 9 |
| ParticipantIDs | wanfang_journals_jsjkxyts201707011 |
| PublicationCentury | 2000 |
| PublicationDate | 2017 |
| PublicationDateYYYYMMDD | 2017-01-01 |
| PublicationDate_xml | – year: 2017 text: 2017 |
| PublicationDecade | 2010 |
| PublicationTitle | 计算机科学与探索 |
| PublicationTitle_FL | Journal of Frontiers of Computer Science & Technology |
| PublicationYear | 2017 |
| Publisher | 西藏大学 藏文信息技术研究中心,拉萨,850000%西南交通大学 信息科学与技术学院,成都,610031%西藏大学 藏文信息技术研究中心,拉萨 850000 西南交通大学 信息科学与技术学院,成都 610031 |
| Publisher_xml | – name: 西藏大学 藏文信息技术研究中心,拉萨,850000%西南交通大学 信息科学与技术学院,成都,610031%西藏大学 藏文信息技术研究中心,拉萨 850000 – name: 西南交通大学 信息科学与技术学院,成都 610031 |
| SSID | ssib054421768 ssib002040941 ssib002423894 ssib051375751 ssib023646573 ssib036438069 ssib002040926 |
| Score | 2.0891247 |
| Snippet | TP391.1; 针对以往进行藏文情感分析时算法忽略藏文语句结构、词序等重要信息而导致结果准确率较低的问题,将深度学习领域内的递归自编码算法引入藏文情感分析中,以更深层次... |
| SourceID | wanfang |
| SourceType | Aggregation Database |
| StartPage | 1122 |
| Title | 深度学习算法在藏文情感分析中的应用研究 |
| URI | https://d.wanfangdata.com.cn/periodical/jsjkxyts201707011 |
| Volume | 11 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources issn: 1673-9418 databaseCode: M~E dateStart: 20070101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://road.issn.org omitProxy: true ssIdentifier: ssib054421768 providerName: ISSN International Centre |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwzV3Na9RQEA-lXryIouI3RXynkpr38vI-jsk2SxH0VKG3ko-NUmEFuwXtwYv146aHakFEeyr1UEEEP_r3dHc9-Tc4M8nuZtsq6slLeHmZN_ObmZCZSd57cZwrvtRp3rKFGxR-4sosSF1rhXJ5UbS4UYUStFn19Rtq7qa8thAsTEz-qM1aWumkM9nqoetK_sWr0Ad-xVWyf-HZIVPogDb4F47gYTj-kY9ZrFikWcRZHLAoZKHCRjhLDckiy0KPxZqFMbOaiH1mA6SxDRYaFhvsN028ZBUzRGN8ZgJqSGabSGwMM4pogI9HnA1KAc42RLJSupXUI4mzRtFlT2hZpOpJMMoFSCEfwwaQgAn2ABM-rggAiAkSjBJIE4GU4bxoGm2REzTCBjGGQREzfEQCHTC4SY1ZNESljx6RkKbWVGCMGV0BgU0yBmGBi2hLUFrXX5qUq0PpBicNAxw0MjHoI1G3gWLTh5lf4hjED6ec4EIjxOkoJSjs2W_amkNIJDhQEEIDFrAkxUOfiIbB_1J4TAQ1fKD0LHlA0h0EbC3NfgkPRXwQ3-_9tQ_6kMaSw8QAKEFEwT46RzQg24YwMA70vzHkdGnGWjhX2netrCL8IN7z2nNN14I3lB6ilghyXn4x3J9k-FobSjJQxMxQBLQgla5Si_Et3JeWl-7cf9BZxjsRQixuRXBEaMiMcarxw3iULkNEtfVyH8_l2LpzqC-G8RP__aCCUfkBp77x1LA8Cbiv8bPq8FxKwXW5OniAupzciCpd_ZVCtJyxXSTtW7XMe_64c6wqmafC8vl3wplYvX3Ssb0vH7u7W92drb1vm_0PG71PL7tvtr9vPO-9etp79Li39q777Env7Yu9rzv912vd3fX--nZ_c73__vMpZ74Zzzfm3Oo3MG6mBHd1VvC0JWWey5bOoEDiWSG8osiTLM88kflJIUwA4QZuzTT3pAa7pGkKdW8eFFIl_mlnsn233TrjTCW4a0CiUtzWUfpSJvibBp0JaaHOauX6rHO50nSxesovLx7w3bk_ITrvHMV2-a72gjPZubfSugjVSye9RC7_CVBD4g0 |
| linkProvider | ISSN International Centre |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E5%9C%A8%E8%97%8F%E6%96%87%E6%83%85%E6%84%9F%E5%88%86%E6%9E%90%E4%B8%AD%E7%9A%84%E5%BA%94%E7%94%A8%E7%A0%94%E7%A9%B6&rft.jtitle=%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%A7%91%E5%AD%A6%E4%B8%8E%E6%8E%A2%E7%B4%A2&rft.au=%E6%99%AE%E6%AC%A1%E4%BB%81&rft.au=%E4%BE%AF%E4%BD%B3%E6%9E%97&rft.au=%E5%88%98%E6%9C%88&rft.au=%E7%BF%9F%E4%B8%9C%E6%B5%B7&rft.date=2017&rft.pub=%E8%A5%BF%E8%97%8F%E5%A4%A7%E5%AD%A6+%E8%97%8F%E6%96%87%E4%BF%A1%E6%81%AF%E6%8A%80%E6%9C%AF%E7%A0%94%E7%A9%B6%E4%B8%AD%E5%BF%83%2C%E6%8B%89%E8%90%A8%2C850000%25%E8%A5%BF%E5%8D%97%E4%BA%A4%E9%80%9A%E5%A4%A7%E5%AD%A6+%E4%BF%A1%E6%81%AF%E7%A7%91%E5%AD%A6%E4%B8%8E%E6%8A%80%E6%9C%AF%E5%AD%A6%E9%99%A2%2C%E6%88%90%E9%83%BD%2C610031%25%E8%A5%BF%E8%97%8F%E5%A4%A7%E5%AD%A6+%E8%97%8F%E6%96%87%E4%BF%A1%E6%81%AF%E6%8A%80%E6%9C%AF%E7%A0%94%E7%A9%B6%E4%B8%AD%E5%BF%83%2C%E6%8B%89%E8%90%A8+850000&rft.issn=1673-9418&rft.volume=11&rft.issue=7&rft.spage=1122&rft.epage=1130&rft_id=info:doi/10.3778%2Fj.issn.1673-9418.1611062&rft.externalDocID=jsjkxyts201707011 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fjsjkxyts%2Fjsjkxyts.jpg |