Keng Hoon Gan and Keat Keong Phang
When accessing structured contents in XML form, information requests are formulated in the form of special query languages such as NEXI, Xquery, etc. However, it is not easy for…
Abstract
Purpose
When accessing structured contents in XML form, information requests are formulated in the form of special query languages such as NEXI, Xquery, etc. However, it is not easy for end users to compose such information requests using these special queries because of their complexities. Hence, the purpose of this paper is to automate the construction of such queries from common query like keywords or form-based queries.
Design/methodology/approach
In this paper, the authors address the problem of constructing queries for XML retrieval by proposing a semantic-syntax query model that can be used to construct different types of structured queries. First, a generic query structure known as semantic query structure is designed to store query contents given by user. Then, generation of a target language is carried out by mapping the contents in semantic query structure to query syntax templates stored in knowledge base.
Findings
Evaluations were carried out based on how well information needs are captured and transformed into a target query language. In summary, the proposed model is able to express information needs specified using query like NEXI. Xquery records a lower percentage because of its language complexity. The authors also achieve satisfactory query construction rate with an example-based method, i.e. 86 per cent (for NEXI IMDB topics) and 87 per cent (NEXI Wiki topics), respectively, compare to benchmark of 78 per cent by Sumita and Iida in language translation.
Originality/value
The proposed semantic-syntax query model allows flexibility of accommodating new query language by separating the semantic of query from its syntax.
Details
Keywords
Customer reviews are one important source that contains valuable information for quality evaluation of products or services. Review sentences contain sentiment words that show…
Abstract
Purpose
Customer reviews are one important source that contains valuable information for quality evaluation of products or services. Review sentences contain sentiment words that show whether a user’s opinion is positive or negative. When review sentence has mix opinions, having sentiment words of both polarities, it is difficult to conclude whether it is positive or negative opinion. The purpose of this study is to improve the detection of polarity in such situation.
Design methodology approach
In this research, methods such as part-of-speech tagging, polarity analysis and rules selection are used to identify the polarity. A set of rules called contrast and conditional polarity rules (CCPR) has been created to improve the polarity detection in cases when there is mixture of sentiment words used in contrast and conditional type of review sentences. The experiment is conducted with data sets from three domains, i.e. restaurant, electronic and Tripadvisor.
Findings
The experimental result confirms that CCPR rules have higher baseline of the polarity aggression. In restaurant domain, CCPR rules (62.07%) have increased 13.79% compared with the Pol_Agg_MPQA baseline (48.28%) and 13.79% compared with Pol_Agg_Senti baseline (48.28%). In electronic domain, CCPR rule (79.17%) is higher by 12.50% compared with the Pol_Agg_MPQA baseline (66.67%) and 16.67% compared with Pol_Agg_Senti baseline (62.50%). Another one, CCPR rule (70.83%) is higher by 8.33% compared with the Pol_Agg_MPQA baseline (62.50%) and 12.50% compared with Pol_Agg_Senti baseline (58.33%). In conclusion, result of experiment shows promising outcome with improvement in detecting the positivity and negativity of indirect sentence, especially for the case of sentence with indirect polarity.
Originality value
To address the problem of mix opinions in terms of polarities, this paper presents a rule-based approach to improve the result of identifying positivity and negativity in sentence with indirect polarities.
Details
Keywords
Issa Alsmadi and Keng Hoon Gan
Rapid developments in social networks and their usage in everyday life have caused an explosion in the amount of short electronic documents. Thus, the need to classify this type…
Abstract
Purpose
Rapid developments in social networks and their usage in everyday life have caused an explosion in the amount of short electronic documents. Thus, the need to classify this type of document based on their content has a significant implication in many applications. The need to classify these documents in relevant classes according to their text contents should be interested in many practical reasons. Short-text classification is an essential step in many applications, such as spam filtering, sentiment analysis, Twitter personalization, customer review and many other applications related to social networks. Reviews on short text and its application are limited. Thus, this paper aims to discuss the characteristics of short text, its challenges and difficulties in classification. The paper attempt to introduce all stages in principle classification, the technique used in each stage and the possible development trend in each stage.
Design/methodology/approach
The paper as a review of the main aspect of short-text classification. The paper is structured based on the classification task stage.
Findings
This paper discusses related issues and approaches to these problems. Further research could be conducted to address the challenges in short texts and avoid poor accuracy in classification. Problems in low performance can be solved by using optimized solutions, such as genetic algorithms that are powerful in enhancing the quality of selected features. Soft computing solution has a fuzzy logic that makes short-text problems a promising area of research.
Originality/value
Using a powerful short-text classification method significantly affects many applications in terms of efficiency enhancement. Current solutions still have low performance, implying the need for improvement. This paper discusses related issues and approaches to these problems.
Details
Keywords
Keng Hoon Gan and Keat Keong Phang
This paper aims to focus on automatic selection of two important structural concepts required in an XML query, namely, target and constraint concepts, when given a keywords query…
Abstract
Purpose
This paper aims to focus on automatic selection of two important structural concepts required in an XML query, namely, target and constraint concepts, when given a keywords query. Due to the diversities of concepts used in XML resources, it is not easy to select a correct concept when constructing an XML query.
Design/methodology/approach
In this paper, a Context-based Term Weighting model that performs term weighting based on part of documents. Each part represents a specific context, thus offering better capturing of concept and term relationship. For query time analysis, a Query Context Graph and two algorithms, namely, Select Target and Constraint (QC) and Select Target and Constraint (QCAS) are proposed to find the concepts for constructing XML query.
Findings
Evaluations were performed using structured document for conference domain. For constraint concept selection, the approach CTX+TW achieved better result than its baseline, NCTX, when search term has ambiguous meanings by using context-based scoring for the concepts. CTX+TW also shows its stability on various scoring models like BM25, TFIEF and LM. For target concept selection, CTX+TW outperforms the standard baseline, SLCA, whereas it also records higher coverage than FCA, when structural keywords are used in query.
Originality/value
The idea behind this approach is to capture the concepts required for term interpretation based on parts of the collections rather than the entire collection. This allows better selection of concepts, especially when a structured XML document consists many different types of information.
Details
Keywords
Shu Schiller, Fiona Fui-Hoon Nah, Andy Luse and Keng Siau
The gender composition of teams remains an important yet complex element in unlocking the success of collaboration and performance in the metaverse. In this study, the authors…
Abstract
Purpose
The gender composition of teams remains an important yet complex element in unlocking the success of collaboration and performance in the metaverse. In this study, the authors examined the collaborations of same- and mixed-gender dyads to investigate how gender composition influences perceptions of the dyadic collaboration process and outcomes at both the individual and team levels in the metaverse.
Design/methodology/approach
Drawing on expectation states theory and social role theory, the authors hypothesized differences between dyads of different gender compositions. A blocked design was utilized where 432 subjects were randomly assigned to teams of different gender compositions: 101 male dyads, 59 female dyads and 56 mixed-gender dyads. Survey responses were collected after the experiment.
Findings
Multilevel multigroup analyses reveal that at the team level, male dyads took on the we-impress manifestation to increase satisfaction with the team solution. In contrast, female and mixed-gender dyads adopted the we-work-hard-on-task philosophy to increase satisfaction with the team solution. At the individual level, impression management is the key factor associated with trust in same-gender dyads but not in mixed-gender dyads.
Originality/value
As one of the pioneering works on gender effects in the metaverse, our findings shed light on two fronts in virtual dyadic collaborations. First, the authors offer a theoretically grounded and gendered perspective by investigating male, female and mixed-gender dyads in the metaverse. Second, the study advances team-based theory and deepens the understanding of gender effects at both the individual and team levels (multilevel) in a virtual collaboration environment.