Skip to main content

Research Repository

Advanced Search

All Outputs (11)

Integrating selection-based aspect sentiment and preference knowledge for social recommender systems. (2019)
Journal Article
CHEN, Y.Y., WIRATUNGA, N. and LOTHIAN, R. 2020. Integrating selection-based aspect sentiment and preference knowledge for social recommender systems. Online information review [online], 44(2), pages 399-416. Available from: https://doi.org/10.1108/OIR-02-2017-0066

Purpose: Recommender system approaches such as collaborative and content-based filtering rely on user ratings and product descriptions to recommend products. More recently, recommender system research has focussed on exploiting knowledge from user-ge... Read More about Integrating selection-based aspect sentiment and preference knowledge for social recommender systems..

Aspect-based sentiment analysis for social recommender systems. (2019)
Thesis
CHEN, Y.Y. 2019. Aspect-based sentiment analysis for social recommender systems. Robert Gordon University [online], PhD thesis. Available from: https://openair.rgu.ac.uk

Social recommender systems harness knowledge from social content, experiences and interactions to provide recommendations to users. The retrieval and ranking of products, using similarity knowledge, is central to the recommendation architecture. To e... Read More about Aspect-based sentiment analysis for social recommender systems..

Effective dependency rule-based aspect extraction for social recommender systems. (2017)
Presentation / Conference
CHEN, Y.Y., WIRATUNGA, N. and LOTHIAN, R. 2017. Effective dependency rule-based aspect extraction for social recommender systems. In Proceedings of the 21st Pacific Asia conference on information systems 2017 (PACIS 2017), 16-20 July 2017, Langkawi, Malaysia. Atlanta: Association for Information Systems [online], article ID 263. Available from: http://aisel.aisnet.org/pacis2017/263

Social recommender systems capitalise on product reviews to generate recommendations that are both guided by experiential knowledge and are explained by user opinions centred on important product aspects. Therefore, having an effective aspect extract... Read More about Effective dependency rule-based aspect extraction for social recommender systems..

Contextual lexicon-based sentiment analysis for social media. (2016)
Thesis
MUHAMMAD, A.B. 2016. Contextual lexicon-based sentiment analysis for social media. Robert Gordon University, PhD thesis.

Sentiment analysis concerns the computational study of opinions expressed in text. Social media domains provide a wealth of opinionated data, thus, creating a greater need for sentiment analysis. Typically, sentiment lexicons that capture term-sentim... Read More about Contextual lexicon-based sentiment analysis for social media..

Contextual sentiment analysis for social media genres. (2016)
Journal Article
MUHAMMAD, A., WIRATUNGA, N. and LOTHIAN, R. 2016. Contextual sentiment analysis for social media genres. Knowledge-based systems [online], 108, pages 92-101. Available from: https://doi.org/10.1016/j.knosys.2016.05.032

The lexicon-based approaches to opinion mining involve the extraction of term polarities from sentiment lexicons and the aggregation of such scores to predict the overall sentiment of a piece of text. It is typically preferred where sentiment labelle... Read More about Contextual sentiment analysis for social media genres..

Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers. (2011)
Journal Article
REGNIER-COUDERT, O., MCCALL, J., LOTHIAN, R., LAM, T., MCCLINTON, S. and N'DOW, J. 2012. Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers. Artificial intelligence in medicine [online], 55(1), pages 25-35. Available from: https://doi.org/10.1016/j.artmed.2011.11.003

Prediction of prostate cancer pathological stage is an essential step in a patient's pathway. It determines the treatment that will be applied further. In current practice, urologists use the pathological stage predictions provided in Partin tables t... Read More about Machine learning for improved pathological staging of prostate cancer: a performance comparison on a range of classifiers..

Related scientific information: a study on user-defined relevance. (2011)
Thesis
BERESI, U.C. 2011. Related scientific information: a study on user-defined relevance. Robert Gordon University, PhD thesis.

This dissertation presents an investigation into the manifestations of relevance observed in the context of related scientific information. The main motivation is to observe if researchers, in the context of knowledge discovery, use different criteri... Read More about Related scientific information: a study on user-defined relevance..

Representation and learning schemes for sentiment analysis. (2009)
Thesis
MUKRAS, R. 2009. Representation and learning schemes for sentiment analysis. Robert Gordon University, PhD thesis.

This thesis identifies four novel techniques of improving the performance of sentiment analysis of text systems. Thes include feature extraction and selection, enrichment of the document representation and exploitation of the ordinal structure of rat... Read More about Representation and learning schemes for sentiment analysis..

Introspective knowledge acquisition for case retrieval networks in textual case base reasoning. (2007)
Thesis
CHAKRABORTI, S. 2007. Introspective knowledge acquisition for case retrieval networks in textual case base reasoning. Robert Gordon University, PhD thesis.

Textual Case Based Reasoning (TCBR) aims at effective reuse of information contained in unstructured documents. The key advantage of TCBR over traditional Information Retrieval systems is its ability to incorporate domain-specific knowledge to facili... Read More about Introspective knowledge acquisition for case retrieval networks in textual case base reasoning..