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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 – 11 of 11 1 On the use of Jargon and Word Embeddings to Explore Subculture within the Reddit’s Manosphere Alani, Harith; Araque, Oscar; Fernandez, Miriam. - 2020 BASE Show details 2 SenZi: A sentiment analysis lexicon for the latinised Arabic (Arabizi) Tobaili, Taha; Fernández, Miriam; Alani, Harith. - : INCOMA, 2019 BASE Show details 3 Contextual semantics for sentiment analysis of Twitter Saif, Hassan; He, Yulan; Fernández, Miriam. - 2016 BASE Show details 4 Contextual semantics for sentiment analysis of Twitter Saif, Hassan; He, Yulan; Fernandez, Miriam. - : Elsevier, 2016 BASE Show details 5 Adapting sentiment lexicons using contextual semantics for sentiment analysis of Twitter Saif, Hassan; He, Yulan; Fernández, Miriam. - : CEUR-WS.org, 2014 BASE Show details 6 Semantic sentiment analysis of twitter Saif, Hassan; He, Yulan; Alani, Harith. - 2012 Abstract: Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics’ feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. “Apple product”) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification. URL: http://oro.open.ac.uk/34929/ http://oro.open.ac.uk/34929/1/76490497.pdf http://iswc2012.semanticweb.org/ https://doi.org/10.1007/978-3-642-35176-1_32 BASE Hide details 7 Automatically extracting polarity-bearing topics for cross-domain sentiment classification Alani, Harith; He, Yulan; Lin, Chenghua. - 2011 BASE Show details 8 The Live Social Semantics application: a platform for integrating face-to-face presence with on-line social networking Van der Broeck, Wouter; Cattuto, Ciro; Barrat, Alain. - 2010 BASE Show details 9 The live social semantics application: A platform for integrating face-to-face presence with on-line social networking Van Der Broeck, Wouter; Cattuto, Ciro; Barrat, Alanin. - 2010 BASE Show details 10 Automatic Ontology-Based Knowledge Extraction from Web Documents Alani, Harith; Kim, Sanghee; Millard, David E.. - 2003 BASE Show details 11 Automatic ontology-based knowledge extraction from web documents Alani, Harith; Kim, Sanghee; Millard, David E.. - 2003 BASE Show details

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