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The development of social media has led to large numbers of internet users now producing massive amounts of user-generated content (UGC). UGC, which shows users’ opinions about events directly, is valuable for monitoring public opinion. Current researches have focused on analysing topic evolutions in UGC. However, few researches pay attention to emotion evolutions of sub-topics about popular events. Important details about users’ opinions might be missed, as users’ emotions are ignored. This paper aims to extract sub-topics about a popular event from UGC and investigate the emotion evolutions of each sub-topic.

This paper first collects UGC about a popular event as experimental data and conducts subjectivity classification on the data to get subjective corpus. Second, the subjective corpus is classified into different emotion categories using supervised emotion classification. Meanwhile, a topic model is used to extract sub-topics about the event from the subjective corpora. Finally, the authors use the results of emotion classification and sub-topic extraction to analyze emotion evolutions over time.

Experimental results show that specific primary emotions exist in each sub-topic and undergo evolutions differently. Moreover, the authors find that performance of emotion classifier is optimal with term frequency and relevance frequency as the feature-weighting method.

To the best of the authors’ knowledge, this is the first research to mine emotion evolutions of sub-topics about an event with UGC. It mines users’ opinions about sub-topics of event, which may offer more details that are useful for analysing users’ emotions in preparation for decision-making.

To better understand the online reviews and help potential consumers, businessmen and product manufacturers effectively obtain users’ evaluation on product aspects, this paper aims to explore the distribution regularities of users’ attention and sentiment on product aspects from the temporal perspective of online reviews.

Temporal characteristics of online reviews (purchase time, review time and time intervals between purchase time and review time), similar attributes clustering and attribute-level sentiment computing technologies are used based on more than 340k smartphone reviews of three products from JD.COM (a famous online shopping platform in China) to explore the distribution regularities of users’ attention and sentiment on product aspects in this paper.

The empirical results show that a power-law distribution can fit users’ attention on product aspects, and the reviews posted in short time intervals contain more product aspects. Besides, the results show that the values of users’ sentiment on product aspects are significantly higher/lower in short time intervals which contribute to judging the advantages and weaknesses of a product.

This paper cannot acquire online reviews for more products with temporal characteristics to verify the findings because of the restriction on reviews crawling by the shopping platforms.

This work reveals the distribution regularities of users’ attention and sentiment on product aspects, which is of great significance in assisting decision-making, optimizing review presentation and improving the shopping experience.

The purpose of this paper is to investigate the superior relationship between blood lipid- and cardiovascular disease (CVD)-related hematological parameters using superior grey relational analysis (GRA).

A total of 294 individuals who underwent simultaneous routine blood examination and blood lipid examination in the Physical Examination Center of the First Affiliated Hospital of Shantou University Medical College were included in this study. Superior GRA was performed to find out the superior factor in CVD-related hematological parameters and blood lipids. CVD-related hematological parameters included red blood cell distribution width, white cell count, and platelet count, platelet distribution width, mean platelet volume, as well as platelet crit. The indicators of blood lipids analyzed here consist of low-density lipoprotein, high-density lipoprotein, triglyceride and total cholesterol.

The results showed that all the grey relational degree of hematological parameters and blood lipids were over 0.8; the superior factor in hematological parameters was PLT, whereas TC was the superior factor in blood lipids.

Findings of this study suggested that hematological parameters are closely related to blood lipids and a potential role for hematological parameters in the prediction of dyslipidemia, which need further study; TC has the greatest influence on hematological parameters, whereas TG displays a minimal impact.

To the authors’ best knowledge, it was the first study to analyze the relationship between various CVD-related hematological parameters and blood lipids via superior GRA.

Through a two-stage survey, this paper examines how researchers judge the quality of answers on ResearchGate Q&A, an academic social networking site.

In the first-stage survey, 15 researchers from Library and Information Science (LIS) judged the quality of 157 answers to 15 questions and reported the criteria that they had used. The content of their reports was analyzed, and the results were merged with relevant criteria from the literature to form the second-stage survey questionnaire. This questionnaire was then completed by researchers recognized as accomplished at identifying high-quality LIS answers on ResearchGate Q&A.

Most of the identified quality criteria for academic answers—such as relevance, completeness, and verifiability—have previously been found applicable to generic answers. The authors also found other criteria, such as comprehensiveness, the answerer's scholarship, and value-added. Providing opinions was found to be the most important criterion, followed by completeness and value-added.

The findings here show the importance of studying the quality of answers on academic social Q&A platforms and reveal unique considerations for the design of such systems.

Websites have their own features in aspect preference (e.g. the relative importance platforms place on product aspects in product evaluation). The purpose of this paper is to capture characteristics of different book reviews on aspect preferences by opinion mining techniques.

The authors employ two indicators for identifying aspect preferences, and propose a method for quantifying overall differences of reviews on aspect preferences through three dimensions: aspect awareness, aspect satisfaction and comprehensive value.

The results show that book reviews on e-commerce websites contain information about external aspects of a book (e.g. hardcover), while those on social network websites pay more attention to content-related aspects of the book (e.g. stories). These results indicate that aspect preferences of reviews vary from platforms and make it hard to evaluate book comprehensively based on single-source data. Online book reviews from a wide range of sources can assess book impact from multiple perspectives and dimensions.

In order to illustrate the value of the authors’ method, the authors show book impact assessment based on multi-source data as an application of these difference analyses. Furthermore, the authors present an example of a book promotion to provide customized marketing services for different user clusters.

This study investigates the influence of different data sources on book evaluation from the content of book reviews. The authors also showcase potential applications of these analyses in book impact assessment.

Terminology is the set of technical words or expressions used in specific contexts, which denotes the core concept in a formal discipline and is usually applied in the fields of machine translation, information retrieval, information extraction and text categorization, etc. Bilingual terminology extraction plays an important role in the application of bilingual dictionary compilation, bilingual ontology construction, machine translation and cross‐language information retrieval etc. This paper aims to address the issues of monolingual terminology extraction and bilingual term alignment based on multi‐level termhood.

A method based on multi‐level termhood is proposed. The new method computes the termhood of the terminology candidate as well as the sentence that includes the terminology by the comparison of the corpus. Since terminologies and general words usually have different distribution in the corpus, termhood can also be used to constrain and enhance the performance of term alignment when aligning bilingual terms on the parallel corpus. In this paper, bilingual term alignment based on termhood constraints is presented.

Experimental results show multi‐level termhood can get better performance than the existing method for terminology extraction. If termhood is used as a constraining factor, the performance of bilingual term alignment can be improved.

The termhood of the candidate terminology and the sentence that includes the terminology is used for terminology extraction, which is called multi‐level termhood. Multi‐level termhood is computed by the comparison of the corpus. Bilingual term alignment method based on termhood constraint is put forward and termhood is used in the task of bilingual terminology extraction. Experimental results show that termhood constraints can improve the performance of terminology alignment to some extent.

The purpose of this paper is to explore which structures of academic articles referees would pay more attention to, what specific content referees focus on, and whether the distribution of PRC is related to the citations.

Firstly, utilizing the feature words of section title and hierarchical attention network model (HAN) to identify the academic article structures. Secondly, analyzing the distribution of PRC in different structures according to the position information extracted by rules in PRC. Thirdly, analyzing the distribution of feature words of PRC extracted by the Chi-square test and TF-IDF in different structures. Finally, four correlation analysis methods are used to analyze whether the distribution of PRC in different structures is correlated to the citations.

The count of PRC distributed in Materials and Methods and Results section is significantly more than that in the structure of Introduction and Discussion, indicating that referees pay more attention to the Material and Methods and Results. The distribution of feature words of PRC in different structures is obviously different, which can reflect the content of referees' concern. There is no correlation between the distribution of PRC in different structures and the citations.

Due to the differences in the way referees write peer review reports, the rules used to extract position information cannot cover all PRC.

The paper finds a pattern in the distribution of PRC in different academic article structures proving the long-term empirical understanding. It also provides insight into academic article writing: researchers should ensure the scientificity of methods and the reliability of results when writing academic article to obtain a high degree of recognition from referees.

As for academic papers, the customary methods for assessing the impact of books are based on citations, which is straightforward but limited to the coverage of databases. Alternative metrics can be used to avoid such limitations, such as blog citations and library holdings. However, content-level information is generally ignored, thus overlooking users’ intentions. Meanwhile, abundant academic reviews express scholars’ opinions on books, which can be used to assess books’ impact via fine-grained review mining. Hence, this study aims to assess books’ use impacts by conducting content mining of academic reviews automatically and thereby confirmed the usefulness of academic reviews to libraries and readers.

Firstly, 61,933 academic reviews in Choice: Current Reviews for Academic Libraries were collected with three metadata metrics. Then, review contents were mined to obtain content metrics. Finally, to identify the reliability of academic reviews, Choice review metrics and other assessment metrics for use impact were compared and analysed.

The analysis results reveal that fine-grained mining of academic reviews can help users quickly understand multi-dimensional features of books, judge or predict the impacts of mass books, so as to provide references for different types of users (e.g. libraries and public readers) in book selection.

Book impact assessment via content mining can provide more detail information for massive users and cover shortcomings of traditional methods. It provides a new perspective and method for researches on use impact assessment. Moreover, this study’s proposed method might also be a means by which to measure other publications besides books.