{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T20:20:37Z","timestamp":1772914837642,"version":"3.50.1"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319635781","type":"print"},{"value":"9783319635798","type":"electronic"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-63579-8_23","type":"book-chapter","created":{"date-parts":[[2017,8,2]],"date-time":"2017-08-02T05:02:42Z","timestamp":1501650162000},"page":"297-310","source":"Crossref","is-referenced-by-count":8,"title":["DFDS: A Domain-Independent Framework for Document-Level Sentiment Analysis Based on RST"],"prefix":"10.1007","author":[{"given":"Zhenyu","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Guozheng","family":"Rao","sequence":"additional","affiliation":[]},{"given":"Zhiyong","family":"Feng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,8,3]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1609\/icwsm.v4i1.14031","volume":"11","author":"B O\u2019Connor","year":"2010","unstructured":"O\u2019Connor, B., Balasubramanyan, R., Routledge, B.R., et al.: From tweets to polls: linking text sentiment to public opinion time series. ICWSM 11, 122\u2013129 (2010)","journal-title":"ICWSM"},{"key":"23_CR2","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.is.2015.06.007","volume":"54","author":"C Musto","year":"2015","unstructured":"Musto, C., Semeraro, G., Lops, P., et al.: CrowdPulse: a framework for real-time semantic analysis of social streams. Inf. Syst. 54, 127\u2013146 (2015)","journal-title":"Inf. Syst."},{"issue":"1","key":"23_CR3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jocs.2010.12.007","volume":"2","author":"J Bollen","year":"2011","unstructured":"Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1\u20138 (2011)","journal-title":"J. Comput. Sci."},{"issue":"1","key":"23_CR4","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.ins.2014.04.034","volume":"285","author":"J Smailovi\u0107","year":"2014","unstructured":"Smailovi\u0107, J., Gr\u010dar, M., Lavra\u010d, N., et al.: Stream-based active learning for sentiment analysis in the financial domain. Inf. Sci. 285(1), 181\u2013203 (2014)","journal-title":"Inf. Sci."},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Aggarwal C., Zhai, C. (eds.) Mining Text Data, pp. 415\u2013463. Springer, US (2012)","DOI":"10.1007\/978-1-4614-3223-4_13"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1-2), 1\u2013135 (2008)","DOI":"10.1561\/1500000011"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Bhatia, P., Ji, Y., Eisenstein, J.: Better document-level sentiment analysis from RST discourse parsing. arXiv preprint arXiv:1509.01599 (2015)","DOI":"10.18653\/v1\/D15-1263"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Mann, W.C., Thompson, S.A.: Rhetorical structure theory: description and construction of text structures. In: Kempen, G. (ed.) Natural Language Generation, pp. 85\u201395. Springer, Netherlands (1987)","DOI":"10.1007\/978-94-009-3645-4_7"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Ji, Y., Eisenstein, J.: Representation learning for text-level discourse parsing. In: Meeting of the Association for Computational Linguistics, pp. 13\u201324, USA (2014)","DOI":"10.3115\/v1\/P14-1002"},{"key":"23_CR10","unstructured":"Corston-Oliver, S.H.: Beyond string matching and cue phrases: improving efficiency and coverage in discourse analysis. In: The AAAI Spring Symposium on Intelligent Text Summarization, pp. 9\u201315 (1970)"},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"Soricut, R., Marcu, D.: Sentence level discourse parsing using syntactic and lexical information. In: Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, pp. 149\u2013156. Association for Computational Linguistics (2004)","DOI":"10.3115\/1073445.1073475"},{"key":"23_CR12","unstructured":"Feng, V.W., Hirst, G.: Text-level discourse parsing with rich linguistic features. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers, vol. 1, pp. 60\u201368. Association for Computational Linguistics (2012)"},{"key":"23_CR13","first-page":"25","volume":"1","author":"S Li","year":"2014","unstructured":"Li, S., Wang, L., Cao, Z., et al.: Text-level discourse dependency parsing. Meet. Assoc. Comput. Linguist. 1, 25\u201335 (2014)","journal-title":"Meet. Assoc. Comput. Linguist."},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Meeting on Association for Computational Linguistics, p. 271. Association for Computational Linguistics (2004)","DOI":"10.3115\/1218955.1218990"},{"issue":"4","key":"23_CR15","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1145\/2432546.2432552","volume":"12","author":"A Sharma","year":"2012","unstructured":"Sharma, A., Dey, S.: A document-level sentiment analysis approach using artificial neural network and sentiment lexicons. ACM SIGAPP Appl. Comput. Rev. 12(4), 67\u201375 (2012)","journal-title":"ACM SIGAPP Appl. Comput. Rev."},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: Conference on Empirical Methods in Natural Language Processing, pp. 1422\u20131432, Portugal (2015)","DOI":"10.18653\/v1\/D15-1167"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Xu, J., Chen, D., Qiu, X., et al.: Cached Long Short-Term Memory Neural Networks for Document-Level Sentiment Classification. arXiv preprint arXiv:1610.04989 (2016)","DOI":"10.18653\/v1\/D16-1172"},{"key":"23_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/978-3-540-76928-6_35","volume-title":"AI 2007: Advances in Artificial Intelligence","author":"K Voll","year":"2007","unstructured":"Voll, K., Taboada, M.: Not all words are created equal: extracting semantic orientation as a function of adjective relevance. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS, vol. 4830, pp. 337\u2013346. Springer, Heidelberg (2007). doi: 10.1007\/978-3-540-76928-6_35"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Heerschop, B., Goossen, F., Hogenboom, A., et al.: Polarity analysis of texts using discourse structure. In: ACM Conference on Information and Knowledge Management. DBLP, pp. 1061\u20131070, Glasgow, United Kingdom (2011)","DOI":"10.1145\/2063576.2063730"},{"key":"23_CR20","unstructured":"Wang, F., Wu, Y., Qiu, L.: Exploiting discourse relations for sentiment analysis. In: COLING: Posters, pp. 1311\u20131320 (2012)"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Li, J., Zhou, Y., Liu, C., et al.: Sentiment classification of Chinese contrast sentences. In: Zong, C., Nie, JY., Zhao, D., Feng, Y. (eds.) Natural Language Processing and Chinese Computing, vol. 496, pp. 205\u2013216. Springer, Heidelberg (2014)","DOI":"10.1007\/978-3-662-45924-9_19"},{"issue":"7","key":"23_CR22","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1145\/2699418","volume":"58","author":"A Hogenboom","year":"2015","unstructured":"Hogenboom, A., Frasincar, F., De Jong, F., et al.: Using rhetorical structure in sentiment analysis. Commun. ACM 58(7), 69\u201377 (2015)","journal-title":"Commun. ACM"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-63579-8_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T20:19:54Z","timestamp":1692908394000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-63579-8_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319635781","9783319635798"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-63579-8_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}