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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林守德 | |
| dc.contributor.author | Chien-Yuan Wang | en |
| dc.contributor.author | 王建元 | zh_TW |
| dc.date.accessioned | 2021-05-20T20:52:52Z | - |
| dc.date.available | 2011-08-20 | |
| dc.date.available | 2021-05-20T20:52:52Z | - |
| dc.date.copyright | 2011-08-09 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-08-03 | |
| dc.identifier.citation | Reference
[1] Cheng-Te Li, Hung-Che Lai, Chien-Tung Ho, Chien-Lin Tseng, Shou-De Lin. 2010 Pusic: musicalize microblog messages for summarization and exploration WWW’10 [2] Lun-Wei Ku and Hsin-Hsi Chen (2007). Mining Opinions from the Web: Beyond Relevance Retrieval. Journal of American Society for Information Science and Technology, Special Issue on Mining Web Resources for Enhancing Information Retrieval, 58(12), pages 1838-1850. Software available at http://nlg18.csie.ntu.edu.tw/opinion/index.html [3] FÅ Nielsen(2011), A new ANEW: Evaluation of a word list for sentiment analysis in microblogs, In International Workshop on Making Sense of Microposts 2011 [4] Sun, Y. T.; Chen, C. L.; Liu, C. C.; Liu, C. L.; and Soo, V. W. 2010. Sentiment Classification of Short Chinese Sentences. In Proceedings of Conference on Computational Linguistics and Speech Processing (ROCLING’10), 184–198. [5] Mei-Yu Chen, Hsin-Ni Lin, Chang-An Shih, Yen-Ching Hsu, Pei-Yu Hsu, Shu-Kai Hsieh. 2010. Classifying Mood in Plurks. In Proceedings of Conference on Computational Linguistics and Speech Processing (ROCLING 2010), 172–183. [6] Go, A.; Bhayani, R.; and Huang, L. 2009. Twitter Sentiment Classification using Distant Supervision. Technical Report, Stanford University. [7] Ekman, P.: An Argument for Basic Emotions. Cognition and Emotion. 6, 169–200 (1992) [8] Barbosa, L., and Feng, J. 2010. Robust Sentiment Detection on Twitter from Biased and Noisy Data. In Proceedings of International Conference on Computational Linguistics (COLING’10), 36–44. [9] Bermingham, A., and Smeaton, A. F. 2010. Classifying Sentiment in Microblogs: is Brevity an Advantage? In Proceedings of ACM International Conference on Information and Knowledge Management (CIKM’10), 1183–1186. [10] Bifet, A., and Frank, E. 2010. Sentiment Knowledge Discovery in Twitter Streaming Data. In Proceedings of International Conference on Discovery Science (DS’10), 1–15. [11] Davidov, D.; Tsur, O.; and Rappoport, A. 2010. Enhanced Sentiment Learning Using Twitter Hashtags and Smileys. In Proceedings of International Conference on Computational Linguistics (COLING’10), 241–249. [12] J. Read. Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In Proceedings of ACL-05, 43nd Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2005. [13] Long Jiang, Mo Yu, Ming Zhou, Xiaohua Liu and Tiejun Zhao. 2011 Target-dependent Twitter Sentiment Classification, ACL2011 [14] P. Calais, A. Veloso, W. Meira Jr. and V. Almeida 2011. From Bias to Opinion: A Transfer-Learning Approach to Real-Time Sentiment Analysis. SIGKDD 2011. [15] Johan Bollen, Alberto Pepe, Huina Mao, 2010. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. WWW2010 [16] Stanford Log-linear Part-Of-Speech Tagger and Stanford Chinese Word Segmenter http://nlp.stanford.edu/software/tagger.shtml http://nlp.stanford.edu/software/segmenter.shtml [17] Changhua Yang, Kevin Hsin-Yih Lin,Hsin-Hsi Chen 2007. Emotion Classification Using Web Blog Corpora. WI '07 Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence [18] Strapparava, C., and Valitutti, A. 2004. Wordnet-affect: an Affective extension of wordnet. In Proceedings of International Conference on Language Resources and Evaluation, 1083–1086. [19] LingPipe http://alias-i.com/lingpipe/ [20] Weka http://www.cs.waikato.ac.nz/ml/weka/ [21] LibSVM and LibLinear http://www.csie.ntu.edu.tw/~cjlin/libsvm/ http://www.csie.ntu.edu.tw/~cjlin/liblinear/ [22] Pak, A., and Paroubek, P. 2010. Twitter as a Corpus for Sentiment Analysis and Opinion Mining. In Proceedings of International Conference on Language Resources and Evaluation (LREC’10), 1320–1326. [23] Li, S.; Zheng, L.; Ren, X.; and Cheng, X. 2009. Emotion Mining Research on Micro-blog. In Proceedings of IEEE Symposium on Web Society, 71–75. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/9972 | - |
| dc.description.abstract | Micro-blog is a popular social platform recently, people shares their life, or comment about something, and all of this contain vast amount of sentiment, it’s a good source we can use to analyze about the feeling of people, like what’s the feeling of people about the new product, is positive or negative. Therefore, sentiment detection is more useful in micro-blog platform, but due to the length constraint, the maximum length of post in micro-blog is only 140 characters, there is not much information than other text genres. So we exploit the property of micro-blog platform to find more information to aid the sentiment detection of post in micro-blog. We focus on three aspects: (a) context, (b) social, (c) response, and propose three approaches, i.e., Feature engineering Based, Graphical model Based, and Markov-transition based , that can exploit the information from the three aspects. Meanwhile, for the purpose of improving the sentiment detection component of Memetube system (original Pusic [1]), which is a platform that can musicalize the sentiment of micro-blogging messages for a given query, based on six basic emotion, so we focus on the six emotion (anger, surprise, sadness, disgust, fear, joy) (Paul Ekman, 1992 [7]), it’s more challenging than positive and negative sentiment. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-20T20:52:52Z (GMT). No. of bitstreams: 1 ntu-100-R98922011-1.pdf: 698517 bytes, checksum: 1266fa20724a395fabed9e004ff321ff (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | Table of Contents
Acknowledgements i 摘要 ii Abstract iii Table of Contents iv List of Figures vi List of Tables vii Chapter 1. Introduction 1 1.1 Background 1 1.2 Motivation and Purpose 2 1.3 Research Statement 3 1.4 Methodology Outline 4 1.5 Contributions 5 1.6 Paper Organization 6 Chapter 2. Related Works 6 Chapter 3. Methodology 9 3.1 Original Supervised learning Model 10 3.2 Feature engineering Based 14 3.3 Graphical model Based 16 3.4 Markov-transition Based 19 Chapter 4. Experiment 23 4.1 Dataset preprocessing and Evaluation method 23 4.2 Evaluation for original supervised learning model 25 4.3 Evaluation for Feature engineering based approach 26 4.4 Evaluation for Graphical model based approach 28 4.5 Evaluation for Markov-transition based approach 28 Chapter 5. Conclusion 32 Reference 33 | |
| dc.language.iso | en | |
| dc.title | 微網誌之短文情緒偵測: 使用時間語境, 社交, 與回應資訊 | zh_TW |
| dc.title | Sentiment Detection of Micro-blogging Short Texts via Contextual, Social, and Responsive Information | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張俊盛,陳信希,劉昭麟,鄭卜壬 | |
| dc.subject.keyword | 情緒偵測,情緒分類, | zh_TW |
| dc.subject.keyword | sentiment detection,sentiment classification, | en |
| dc.relation.page | 35 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2011-08-04 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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