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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 15 of 15 1 First year of the UQ sustainable energy micromasters series: Evaluation of participation and achievement Coffey, Felicity C.; Ashworth, Peta. - : Springer Verlag, 2019 BASE Show details 2 CLEF ehealth 2019 evaluation lab Kelly, Liadh; Goeuriot, Lorraine; Suominen, Hanna. - : Springer, 2019 BASE Show details 3 Overview of the CLEF eHealth Evaluation Lab 2019 Kelly, Liadh; Suominen, Hanna; Goeuriot, Lorraine. - : Springer Verlag, 2019 BASE Show details 4 Leveraging knowledge graph embeddings for natural language question answering Wang, Ruijie; Wang, Meng; Liu, Jun. - : Springer, 2019 BASE Show details 5 Using LIP to gloss over faces in single-stage face detection networks Yang, Siqi; Wiliem, Arnold; Chen, Shaokang. - : Springer, 2018 BASE Show details 6 Vertical and sequential sentiment analysis of micro-blog topic Wan, Shuo; Li, Bohan; Zhang, Anman; Wang, Kai; Li, Xue. - : Springer, 2018 Abstract: Sentiment analysis of micro-blog topic aims to explore people’s attitudes towards a topic or event on social networks. Most existing research analyzed the micro-blog sentiment by traditional algorithms such as Naive Bayes and SVM based on the manually labelled data. They do not consider timeliness of data and inwardness of the topics. Meanwhile, few Chinese micro-blog sentiment analysis based on large-scale corpus is investigated. This paper focuses on the analysis of sequential sentiment based on a million-level Chinese micro-blog corpora to mine the features of sequential sentiment precisely. Distant supervised learning method based on micro-blog expressions and sentiment lexicon is proposed and fastText is used to train word vectors and classification model. The timeliness of analysis is guaranteed on the premise of ensuring the accuracy of classifier. The experiment shows that the accuracy of the classifier reaches 92.2%, and the sequential sentiment analysis based on this classifier can accurately reflect the emotional trend of micro-blog topics. Keyword: 1700 Computer Science; 2614 Theoretical Computer Science; Distant supervision; fastText; Sequential analysis; Vertical sentiment analysis URL: https://espace.library.uq.edu.au/view/UQ:85e76c4 BASE Hide details 7 DAVE: extracting domain attributes and values from text corpus Shen, Yongxin; Li, Zhixu; Zhang, Wenling. - : Springer Verlag, 2018 BASE Show details 8 Mining the context of citations in scientific publications Hassan, Saeed-Ul; Iqbal, Sehrish; Imran, Mubashir. - : Springer, 2018 BASE Show details 9 Overview of the CLEF eHealth evaluation lab 2018 Suominen, Hanna; Kelly, Liadh; Goeuriot, Lorraine. - : Springer International Publishing, 2018 BASE Show details 10 The JStar language philosophy Utting, Mark; Weng, Min-Hsien; Cleary, John G.. - : Elsevier, 2014 BASE Show details 11 Structural operational semantics through context-dependent behaviour Colvin, Robert J.; Hayes, Ian J.. - : Elsevier North-Holland, 2011 BASE Show details 12 Information flow analysis with Chinese text Cheong, Paulo; Song, Dawei; Bruza, Peter. - : Springer Verlag, 2005 BASE Show details 13 A Correlation Analysis on LSA and HAL Semantic Space Models Yan, Xin; Li, Xue; Song, Dawei. - 2004 BASE Show details 14 Fuzzy K-means clustering on a high dimensional semantic space Cao, Guihong; Song, Dawei; Bruza, Peter. - : Springer Verlag, 2004 BASE Show details 15 A natural language interpreter for the construction of conceptual schemas Dunn, Leone; Orlowska, Maria. - : Springer Verlag, 1990 BASE Show details

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