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First year of the UQ sustainable energy micromasters series: Evaluation of participation and achievement
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Leveraging knowledge graph embeddings for natural language question answering
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Using LIP to gloss over faces in single-stage face detection networks
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Vertical and sequential sentiment analysis of micro-blog topic
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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.
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Keyword:
1700 Computer Science; 2614 Theoretical Computer Science; Distant supervision; fastText; Sequential analysis; Vertical sentiment analysis
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URL: https://espace.library.uq.edu.au/view/UQ:85e76c4
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DAVE: extracting domain attributes and values from text corpus
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A Correlation Analysis on LSA and HAL Semantic Space Models
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Fuzzy K-means clustering on a high dimensional semantic space
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A natural language interpreter for the construction of conceptual schemas
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