Dr Jenny Carter

Job: Principal Lecturer

Faculty: Technology

School/department: School of Computer Science and Informatics

Research group(s): Centre for Computational Intelligence (CCI)

Address: De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom

T: +44 (0)116 250 6449

E: jennyc@dmu.ac.uk

W: www.dmu.ac.uk/digits

 

Personal profile

Jenny Carter is a Principal Lecturer in the School of Computer science & Informatics and she is Subject Group Leader for Computer Science & Security, Intelligent Systems & Games.

She teaches fuzzy logic, data mining, knowledge based systems, applications of artificial intelligence (AI), database design and development. She supervises PhD students in AI related areas and in particular, where AI is applied to Pedagogic problems.

Research group affiliations

Publications and outputs 

  • Application of uninorms to market basket analysis
    Application of uninorms to market basket analysis Moodley, Raymond; Chiclana, Francisco; Caraffini, Fabio; Carter, Jenny The ability for grocery retailers to have a single view of customers across all their grocery purchases remains elusive and has become increasingly important in recent years (especially in the UK) where competition has intensified, shopping habits and demographics have changed and price sensitivity has increased following the 2008 recession. Numerous studies have been conducted on understanding independent items that are frequently bought together (association rule mining/ frequent itemsets) with several measures proposed to aggregate item support and rule confidence with varying levels of accuracy as these measures are highly context dependent. Uninorms were used as an alternative measure to aggregate support and confidence in analysing market basket data using the UK grocery retail sector as a case study. Experiments were conducted on consumer panel data with the aim of comparing the uninorm against three other popular measures (Jaccard, Cosine and Conviction). It was found that the uninorm outperformed other models on its adherence to the fundamental monotonicity property of support in market basket analysis. Future work will include the extension of this analysis to provide a generalised model for market basket analysis. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Distance Learning: Lessons Learned from a UK Masters Programme
    Distance Learning: Lessons Learned from a UK Masters Programme Carter, Jenny; Chiclana, Francisco The MSc Intelligent Systems (IS) and the MSc Intelligent Systems and Robotics (ISR) programmes at De Montfort University are Masters courses that are delivered both on-site and by distance learning. The courses have been running successfully on-site for over 10 years. Designing and delivering courses as distance learning presents a challenge, particularly where the content includes technical and practical elements. For this work we look back at some of the techniques that have been adopted and consider their success or otherwise at enabling us to overcome these challenges. We reflect on some previous studies that have been undertaken over the years of running the courses. Finally, the lessons learned from these successful programmes are considered with a view to generalising the approach and more specifically how we can apply our experiences to the development of new distance courses in Data Analytics and in Artificial Intelligence at Huddersfield University.
  • IOWA & Cross-ratio Uninorm operators as aggregation tools in sentiment analysis and ensemble methods
    IOWA & Cross-ratio Uninorm operators as aggregation tools in sentiment analysis and ensemble methods Appel, Orestes; Chiclana, Francisco; Carter, Jenny; Fujita, Hamido In the field of Sentiment Analysis, a number of different classifiers are utilised to attempt to establish the polarity of a given sentence. As such, there could be a need for aggregating the outputs of the algorithms involved in the classification effort. If the output of every classification algorithm resembles the opinion of an expert in the subject at hand, we are then in the presence of a group decision making problem, which in turn translates into two sub-problems: (a) defining the desired semantic of the aggregation of all opinions, and (b) applying the proper aggregation technique that can achieve the desired semantic chosen in (a). The objective of this article is twofold. Firstly, we present two specific aggregation semantics, namely fuzzy-majority and compensatory, which are based on Induced Ordered Weighted Averaging and Uninorm operators, respectively. Secondly, we show the power of these two techniques by applying them to an existing hybrid method for classification of sentiments at the sentence level. In this case, the proposed aggregation solutions act as a complement in order to improve the performance of the aforementioned hybrid method. In more general terms, the proposed solutions could be used in the creation of semantic-sensitive ensemble methods, instead of the more simple ensemble choices available today in commercial machine learning software offerings. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Successes and challenges in developing a hybrid approach to sentiment analysis
    Successes and challenges in developing a hybrid approach to sentiment analysis Appel, Orestes; Chiclana, Francisco; Carter, Jenny; Fujita, Hamido This article covers some success and learning experiences attained during the developing of a hybrid approach to Sentiment Analysis (SA) based on a Sentiment Lexicon, Semantic Rules, Negation Handling, Ambiguity Management and Linguistic Variables. The proposed hybrid method is presented and applied to two selected datasets: Movie Review and Sentiment Twitter datasets. The achieved results are compared against those obtained when Nai ve Bayes (NB) and Maximum Entropy (ME) supervised machine learning classification methods are used for the same datasets. The proposed hybrid system attained higher accuracy and precision scores than NB and ME, which shows its superiority when applied to the SA problem at the sentence level. Finally, an alternative strategy to calculating the orientation polarity and polarity intensity in one step instead of the two steps method used in the hybrid approach is explored. The analysis of the yielded mixed results achieved with this alternative approach shows its potential as an aid in the computation of semantic orientations and produced some lessons learnt in developing a more effective mechanism to calculating the orientation polarity and polarity intensity. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • A consensus approach to sentiment analysis
    A consensus approach to sentiment analysis Appel, Orestes; Chiclana, Francisco; Carter, Jenny; Fujita, Hamido There are many situations where the opinion of the majority of participants is critical. The scenarios could be multiple, like a number of doctors finding commonality on the diagnosing of an illness or parliament members looking for consensus on a specific law being passed. In this article we present a method that utilises Induced Ordered Weighted Averaging (IOWA) operators to aggregate a majority opinion from a number of Sentiment Analysis (SA) classification systems, where the latter occupy the role usually taken by human decision-makers. Previously determined sentence intensity polarity by different SA classification methods are used as input to a specific IOWA operator. During the experimental phase, the use of the IOWA operator coupled with the linguistic quantifier `most' (IOWA_most) proved to yield superior results compared to those achieved when utilising other techniques commonly applied when some sort of averaging is needed, such as arithmetic mean or median techniques.
  • Cross-ratio uninorms as an effective aggregation mechanism in Sentiment Analysis
    Cross-ratio uninorms as an effective aggregation mechanism in Sentiment Analysis Appel, Orestes; Chiclana, Francisco; Carter, Jenny; Fujita, Hamido There are situations in which lexicon-based methods for Sentiment Analysis (SA) are not able to generate a classification output for specific instances of a given dataset. Most often, the reason for this situation is the absence of specific terms in the sentiment lexicon required in the classification effort. In such cases, there were only two possible paths to follow: (1) add terms to the lexicon (off-line process) by human intervention to guarantee no noise is introduced into the lexicon, which prevents the classification system to provide an immediate answer; or (2) use the services of a word-frequency dictionary (on-line process), which is computationally costly to build. This paper investigates an alternative approach to compensate for the lack of ability of a lexicon-based method to produce a classification output. The method is based on the combination of the classification outputs of non lexicon-based tools. Specifically, firstly the outcome values of applying two or more non-lexicon classification methods are obtained. Secondly, these non-lexicon outcomes are fused using a uninorm based approach, which has been proved to have desirable compensation properties as required in the SA context, to generate the classification output the lexicon based approach is unable to achieve. Experimental results based on the execution of two well-known supervised machine learning algorithms, namely Na\"{i}ve Bayes and Maximum Entropy, and the application of a cross-ratio uninorm operator are presented. Performance indices associated to options (1) and (2) above are compared against the results obtained using the proposed approach for two different datasets. Additionally, the performance of the proposed cross-ratio uninorm operator based approach is also compared when the aggregation operator used is the arithmetic mean instead. It is shown that the combination of non lexicon-based classification methods with specific uninorm operators improves the classification performance of lexicon-based methods, and it enables the offering of an alternative solution to the SA classification problem when needed. The proposed aggregation method could be used as well as a replacement of ensemble averaging techniques commonly applied when combining the results of several machine learning classifiers' outputs. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • A consensus approach to the sentiment analysis problem driven by support-based IOWA majority
    A consensus approach to the sentiment analysis problem driven by support-based IOWA majority Appel, Orestes; Chiclana, Francisco; Carter, Jenny; Fujita, Hamido In group decision-making there are many situations where the opinion of the majority of participants is critical. The scenarios could be multiple, like a number of doctors finding commonality on the diagnose of an illness or parliament members looking for consensus on an specific law being passed. In this article we present a method that utilises Induced Ordered Weighted Averaging (IOWA) operators to aggregate a majority opinion from a number of Sentiment Analysis (SA) classification systems, where the latter occupy the role usually taken by human decision-makers as typically seen in group decision situations. In this case, the numerical outputs of different SA classification methods are used as input to a specific IOWA operator that is semantically close to the fuzzy linguistic quantifier 'most of'. The object of the aggregation will be the intensity of the previously determined sentence polarity in such a way that the results represents what the majority think. During the experimental phase, the use of the IOWA operator coupled with the linguistic quantifier 'most' (IOWA_most) proved to yield superior results compared to those achieved when utilising other techniques commonly applied when some sort of averaging is needed, such as arithmetic mean or median techniques.
  • A Hybrid Approach for Supporting Adaptivity in E-learning Environments
    A Hybrid Approach for Supporting Adaptivity in E-learning Environments Al-Omari, Mohammad; Carter, Jenny; Chiclana, Francisco Purpose: The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the ECA model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity in any given Learning Management System based on learners’ learning styles. Design/methodology/approach: This paper offers a brief review of current frameworks and systems to support adaptivity in e-learning environments. A framework to support adaptivity is designed and discussed, reflecting the hybrid approach in detail. A system prototype is developed incorporating different adaptive features based on the Felder-Silverman learning styles model. Finally, the prototype is implemented in Moodle. Findings: The system prototype supports real-time adaptivity in any given Learning Management System based on learners’ learning styles. It can deal with any type of content provided by course designers and instructors in the Learning Management System. Moreover, it can support adaptivity at both course and learner levels. Research limitations/implications: Practical implications: Social implications: Originality/value: To the best of our knowledge, no previous work has been done incorporating the concept of the ECA model and intelligent agents as hybrid architecture to support adaptivity in e-learning environments. The system prototype has wider applicability and can be adapted to support different types of adaptivity.
  • A Hybrid Approach to the Sentiment Analysis Problem at the Sentence Level
    A Hybrid Approach to the Sentiment Analysis Problem at the Sentence Level Appel, Orestes; Chiclana, Francisco; Carter, Jenny; Fujita, Hamido The objective of this article is to present a hybrid approach to the Sentiment Analysis problem at the sentence level. This new method uses natural language processing (NLP) essential techniques, a sentiment lexicon enhanced with the assistance of SentiWordNet, and fuzzy sets to estimate the semantic orientation polarity and its intensity for sentences, which provides a foundation for computing with sentiments. The proposed hybrid method is applied to three different data-sets and the results achieved are compared to those obtained using Naïve Bayes and Maximum Entropy techniques. It is demonstrated that the presented hybrid approach is more accurate and precise than both Naïve Bayes and Maximum Entropy techniques, when the latter are utilised in isolation. In addition, it is shown that when applied to datasets containing snippets, the proposed method performs similarly to state of the art techniques. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • A Hybrid Approach to Sentiment Analysis
    A Hybrid Approach to Sentiment Analysis Appel, Orestes; Chiclana, Francisco; Carter, Jenny; Fujita, Hamido This contribution presents a hybrid approach to Sentiment Analysis (SA) encompassing the use of semantic rules, fuzzy sets, unsupervised machine learning techniques and a sentiment lexicon improved with the support of Senti-WordNet. A Hybrid Standard Classification is first carried out, which is further enhanced into a Hybrid Advanced approach incorporating linguistic classification of semantic polarity modelled using fuzzy sets. The mechanism of the new SA methodology is illustrated by applying it to compute the polarity of a given sentence and to a benchmarking publicly available dataset: the Movie Review Dataset.

Click here to view a full listing of Jenny Carter's publications and outputs.

Areas of teaching

  1. Fuzzy Logic
  2. Data Mining
  3. Knowledge Based Systems
  4. Applied Computational Intelligence
  5. Database Design and Development

Qualifications

  1. PhD – Inductive Learning in Musical Style Analysis
  2. MSc – Information Technology
  3. BSc Chemistry & Mathematics
  4. PGCE – Mathematics Teaching

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