Keyness

In this paper we examine the definitions of two widely-used interrelated constructs in corpus linguistics, keyness and keywords, as presented in the literature and corpus software manuals. In particular, we focus on • the consistency of definitions given in different sources; • the metrics used to calculate the level of keyness; • the compatibility between definitions and metrics. Our survey of studies employing keyword analysis has indicated that the vast majority of studies examine a subset of keywords-almost always the top X number of keywords as ranked by the metric used. This renders the issue of the appropriate metric central to any study using keyword analysis. In this study, we first argue that an appropriate, and therefore useful, metric for keyness needs to be fully consistent with the definition of keyword. We then use four sets of comparisons between corpora of different types and sizes, in order to test whether and to what extent the use of different metrics affects the ranking of keywords. More precisely, we look at the extent of overlap in the keyword rankings resulting from the adoption of different metrics, and we discuss the implications of ranking-based analysis adopting one metric or another. Finally, we propose a new metric for keyness, and demonstrate a simple way to calculate the metric, which supplements the keyword extraction in existing corpus software.

In this study, we introduce a model that captures and visualizes the dynamical process of individual intention forming and the translation of this intention into actual behavior when learning in MOOCs. To validate the model and further our understanding of learning in MOOCs, we constructed a short survey based on this theoretically grounded intention-behavior dynamics model. This survey was sent to MOOC learners who at the time of their respective MOOCs indicated that we could contact them for further research purposes. The combination of open and closed questions referred to the most recent MOOC they took and was answered by 84 learners. The results revealed that most learners start a MOOC with a specific intention in mind, but that nearly one third of these learners reformulates this initial intention, once or more often, at some point due to barriers they encounter which hinder or prevent them from reaching their individual intentions. These barriers are mainly non-MOOC related, which may be valuable input for future research as well as guide the development of interventions for supporting learners to reach their personal learning intentions.

MOOC-learning can be challenging as barriers which prevent or hinder acting out MOOC-takers' individual learning intentions may be encountered. The aim of this research was to elicit and to empirically classify barriers that influence this intention achievement in MOOCs. The best fit model of our factor-analytical approach resulted in 4 distinctive components; 1. Technical and online-learning related skills, 2. Social context, 3. Course design/ expectations management, 4. Time, support and motivation. The main finding of our study is that the experienced barriers by MOOC-takers are predominantly non-MOOC related. This knowledge can be of value for MOOC-designers and providers. It may guide them in finding suitable redesign solutions or interventions to support MOOC-takers in their learning, even if it concerns non-MOOC related issues. Furthermore, it makes a valuable contribution to the expanding empirical research on MOOCs.

Every language contains a number of linguistic units that serve as a key to understanding the culture of its language speakers. Therefore, they bear specific information that helps to reveal cultural similarities and dissimilarities. Taking into account contemporary studies and focus of the science, this information can be very critical and relevant in intercultural communication. In this paper, we name such linguistic units as cultural keywords. So, we claim that cultural keywords can serve as a tool for intercultural communication. They are indicators for describing and understanding cultures. This can help to study, learn, discuss and describe the cultures of different nations all over the world. In the paper we suggest methodologies to identify cultural keywords. We claim that a methodological basis for the study may consist of three methods: semantic analysis, argumentative analysis and the Zipf's law. However, these are not comprehensive methods for discovering the keywords. Findings of the research can be applied to intercultural and cross-cultural studies as well as other interdisciplinary cultural studies.

Every language contains a number of linguistic units that serve as a key to understanding the culture of its language speakers. Therefore, they bear specific information that helps to reveal cultural similarities and dissimilarities. Taking into account contemporary studies and focus of the science, this information can be very critical and relevant in intercultural communication. In this paper, we name such linguistic units as cultural keywords. So, we claim that cultural keywords can serve as a tool for intercultural communication. They are indicators for describing and understanding cultures. This can help to study, learn, discuss and describe the cultures of different nations all over the world. In the paper we suggest methodologies to identify cultural keywords. We claim that a methodological basis for the study may consist of three methods: semantic analysis, argumentative analysis and the Zipf's law. However, these are not comprehensive methods for discovering the keywords. Findings of the research can be applied to intercultural and cross-cultural studies as well as other interdisciplinary cultural studies.

The article reports research on the concept of key words as statistically significant items in a text or corpus. It reviews approaches to eliciting key words used in various software products for language analysis and the rationale for adopting them. Based on empirical data, a new method is proposed and tested on an exploratory corpus. The motivation and arguments for proposing the procedure are revealed, using comparisons between different languages. The adequacy of the results yielded by the different methods is tested via a mechanism developed with this research.

Learners join MOOCs (Massive Open Online Courses) with a variety of intentions. The fulfillment of these initial intentions is an important success criterion in self-paced and open courses. Using post course self-reported data enabled us to divide the participants to those who fulfilled the initial intentions (high-IF) and those who did not fulfill their initial intentions (low-IF). We used methods adapted from natural language processing (NLP) to analyze the learning paths of 462 MOOC participants and to identify activities and activity sequences of participants in the two groups. Specifically, we used n-gram analysis to identify learning activity sequences and keyness analysis to identify prominent learning activities. These measures enable us to identify the differences between the two groups. Differences can be seen at the level of single activities, but major differences were found when longer n-grams were used. The high-IF group showed more consistency and less divergent learning behavior. High-IF was associated, among other things, with study patterns of sequentially watching video lectures. Theoretical and practical suggestions are introduced in order to help MOOC developers and participants to fulfill the participants' learning intentions.

With its tourism industry becoming the main source of income of the country, Thailand has put an emphasis on promoting tourist destinations to foreign tourists via different channels including online resources. The Tourism Authority of Thailand (TAT), a state agency in charge of tourism promotion, provides useful information of attractions in Thai and other languages on their webpages. This paper attempts to analyse the tourism discursive elements as posited by Durán Muñoz (2012) and Dann (1996) in the English version of the TAT webpages on Bangkok and to analyse how these components are translated. The study found few uses of the tourism language characteristic in the translated version as the result of the moderate uses of these constituents and the exclusion of certain Thai texts in the translation. The investigation also found that these linguistic tools are both literally translated and adapted. While the original meaning is preserved by literal translation, adaptation tends to heighten the tourism discourse quality in the translated text yet alters the original meaning.

In this paper, we present a simple but efficient approach for the automatic mood classification of microblogging messages from Plurk platform. In contrast with Twitter, Plurk has become the most popular microblogging service in Taiwan and other countries

In this paper, we present a simple but efficient approach for the automatic mood classification of microblogging messages from Plurk platform. In contrast with Twitter, Plurk has become the most popular microblogging service in Taiwan and other countries 1; however, no previous research has been done for the emotion and mood recognition, nor the Chinese affective terms or corpus available. Following the line of mashup programming, we thus construct a dynamic plurk corpus by pipelining Plurk APIs, Yahoo! Chinese ...

In this paper, we present a simple but efficient approach for the automatic mood classification of microblogging messages from Plurk platform. In contrast with Twitter, Plurk has become the most popular microblogging service in Taiwan and other countries 1 ; however, no previous research has been done for the emotion and mood recognition, nor the Chinese affective terms or corpus available. Following the line of mashup programming, we thus construct a dynamic plurk corpus by pipelining Plurk APIs, Yahoo! Chinese segmentation APIs, etc to preprocess and annotate the corpus data. Based on the corpus, we conduct experiments by way of combining textual statistics and emoticons data, and our method yield the results with high performance. This work can be further extended to combine with affective ontology designed with emotion theory of appraisal.

The purpose of this paper is to analyse and, if necessary, qualify the high incidence of evaluative adjectives presumably to be expected in promotional hotel websites. Thus, the aim of the study is to understand the role they play in that genre, the way they work, their actual usage and possible classi¿ cation, as well as the role they take in persuading the reader. This corpus-based study of evaluative adjectives was therefore grounded on the analysis of their syntactic behaviour, their recurrent collocational patterns and on drawing up of a proposed classi¿ cation with which to better comprehend the entities and acts that best represent the domain. At the same time, it will also provide a clearer understanding of the way they are portrayed in the genre (hotel websites) and in the type of discourse being analysed (promotional).

Semantic Pathways is a corpus exploration tool with a unique visual interface in which keyword extraction and keyword-based document clustering have been implemented in order to facilitate insight forming. Semantic Pathways combines corpus comparison techniques from Corpus Linguistics with aestheticallydriven design and interaction, to produce fluidly interactive information exploration. In addition, users can access Semantic Pathways via a command-line interface, where integration with Python and NLTK offers additional benefits. We describe system operation from the user’s perspective, and then use the tool for corpus comparison of different varieties of English with the LOB and Brown corpora as test and reference sets, demonstrating its novelty in gisting an entire document collection and speedy and intuitive exploration of lexical usage across the document set.

In this paper, we present a simple but efficient approach for the automatic mood classification of microblogging messages from Plurk platform. In contrast with Twitter, Plurk has become the most popular microblogging service in Taiwan and other countries 1 ; however, no previous research has been done for the emotion and mood recognition, nor the Chinese affective terms or corpus available. Following the line of mashup programming, we thus construct a dynamic plurk corpus by pipelining Plurk APIs, Yahoo! Chinese segmentation APIs, etc to preprocess and annotate the corpus data. Based on the corpus, we conduct experiments by way of combining textual statistics and emoticons data, and our method yield the results with high performance. This work can be further extended to combine with affective ontology designed with emotion theory of appraisal. Keyword: mood classification, plurks, keyness, emotion paradox 1 According to Alvin, the cofounder of Plurk website, the number of the plurke...

In this paper we examine the definitions of two widely-used interrelated constructs in corpus linguistics, keyness and keywords, as presented in the literature and corpus software manuals. In particular, we focus on • the consistency of definitions given in different sources; • the metrics used to calculate the level of keyness; • the compatibility between definitions and metrics. Our survey of studies employing keyword analysis has indicated that the vast majority of studies examine a subset of keywords-almost always the top X number of keywords as ranked by the metric used. This renders the issue of the appropriate metric central to any study using keyword analysis. In this study, we first argue that an appropriate, and therefore useful, metric for keyness needs to be fully consistent with the definition of keyword. We then use four sets of comparisons between corpora of different types and sizes, in order to test whether and to what extent the use of different metrics affects the ranking of keywords. More precisely, we look at the extent of overlap in the keyword rankings resulting from the adoption of different metrics, and we discuss the implications of ranking-based analysis adopting one metric or another. Finally, we propose a new metric for keyness, and demonstrate a simple way to calculate the metric, which supplements the keyword extraction in existing corpus software.

The paper comes from the need to search for criteria useful for the valorization of heritage towns, located in rural and/or inland areas of Italy, now affected by depression and depopulation process. To this end, the authors point out how territorial identity can constitute the theoretical foundation to influence development policies and, in particular, tourism development for the sustainability process. It was therefore decided to interview a number of stakeholders who could contribute, with their professionalism and expertise, to identifying possible paths and processes for the enhancement of these areas for tourism development. The methodology was based on in-depth interviews, which allowed for the identification of a of a Strength, Weakness, Opportunities and Threat (SWOT) analysis, offering a guideline for the correct governance of these rural areas for their tourist enhancement in terms of the sustainability of development and tourist attractiveness. The study is an observatory that will monitor the implementation of sustainable tourism enhancement of the "borghi".

Although there has been plenty of research on newspaper discourse in stylistics and discourse analysis, there is not much that has integrated a stylistic framework (critical stylistics) with corpus linguistics tools. For this reason, Keywords in the Press will help our understanding both of the socio-political keywords used in UK broadsheets during the Blair years, and of some methodological implications in the combination of both disciplines. The research reported in this book relies heavily on the critical stylistics framework developed previously by Jeffries (2010) to analyse individual lexical items, with the overall aim of searching for possible ideological meanings which emerged and/or changed during that period, but also demonstrating that corpus methods can ably assist in that search.

This presentation provides a critical examination of the following: the nature of keyness, the types and foci of keyness analysis, possible units of a keyness analysis, the principled selection of corpora, appropriate metrics for establishing the level of keyness, principled procedures for selecting key items for further analysis. The above are also demonstrated via an example study.

Keyness analysis is perhaps the most widely used technique within corpus approaches to (critical) discourse studies. As an automated keyness analysis usually returns a much larger number of key items than is feasible to examine manually within an appropriate co-text, the approach to selection of key items is of paramount importance, as it will determine the results and conclusions (Gabrielatos & Marchi, 2011). Currently, studies tend to adopt a methodologically naïve approach to selecting key items for manual analysis: they remove items from consideration before the automated analysis  by using frequency thresholds or stoplists and/or or select a small sub-set of items returned by the automated analysis (e.g. the top-N key items and/or key items that they deem relevant to the focus of the study) (see Pojanapunya & Watson Todd, 2016). However, the above approaches lack a principled rationale, and adopting them can remove important key items from consideration and lead to cherry-picking – consequently rendering results and conclusions questionable. Also, keyness studies predominantly focus on differences between the compared corpora, and there are very few studies using keyness analysis to examine similarities (Taylor, 2013). This paper will discuss a new approach to selecting key items in a principled fashion, and will demonstrate the relevant procedures via a case study. The approach utilises cluster analysis, and caters for a focus on both difference and similarity. However, in order to contextualise the proposed procedure, the paper will need to preface its main focus with addressing a number of relevant misconceptions regarding the nature of keyness, the selection of the corpora to be compared (usually referred to as the study and reference corpus), and appropriate metrics for establishing keyness.

Keyness analysis is perhaps the most widely used technique within corpus approaches to (critical) discourse studies. This chapter will first define the nature of keyness, and outline the research foci that keyness analysis can be usefully employed to address (e.g. not only differences, but also similarities). It will then provide a brief historical overview of the use of frequency comparisons in corpus linguistics, from manual comparisons of the frequency of particular linguistic items to the automated comparison of the frequency of all words in two corpora (or sub-corpora). The above will also act as the springboard for the discussion of the core aspects of keyness analysis, and associated common misconceptions.

1. The unit of analysis. Most studies select the word as the unit of analysis (as indicated by the common use of the term ‘keyword’). However, this need not be the default selection: frequency comparisons can focus on any lexical or morphosyntactic unit, or particular semantic or pragmatic meanings – as long as the appropriate corpus annotation has been carried out.
2. The selection of the corpora to be compared. The discussion will focus on issues relating to the nature of ‘study’ and ‘reference’ corpus, and their relative size. 
3. The issue of appropriate metrics. The discussion will focus on the difference between metrics of effect size and statistical significance, and the usefulness of combining the two types. It will also provide a comparison of the effect size metrics currently adopted by widely used corpus tools.
4. The selection of the key items that a study can usefully focus on. As an automated keyness analysis usually returns a much larger number of key items than is feasible to tackle, the approach to selection is of paramount importance, as it will determine the results and conclusions.

These issues, and the associated methodological decisions and their implications, will be discussed with reference to a case study.

Length motifs (L-motifs) are defined as sequences of words whose lengths are monotonously increasing. In recent years, L-motifs have attracted well-deserved attention as they provide a new view of texts and their syntagmatic properties and nested structures. This study examines the key L-motifs, i.e. motifs that are overrepresented in texts and negative key L-motifs that are underrepresented in texts. The data reveal motifs that are typical for Czech texts, motifs that are typical for Arabic texts, and motifs that are typical for both Czech and Arabic texts – their existence suggests that there are new general language-independent patterns waiting to be explored.

The article reports research on the concept of key words as words statistically significant in a text or corpus. It reviews approaches to eliciting key words used in various software products for language analysis and the rationale for adopting them. Based on empirical data, a new method is proposed and tested on an exploratory corpus. The motivation and arguments for proposing the procedure are revealed, using comparisons between different languages. The adequacy of the results yielded by the different methods is tested via a mechanism developed with this research.

Fandom is a growing phenomenon in the contemporary user-generated mediascape, inasmuch as it is capitalizing on the unprecedented possibilities of publication, distribution and interaction made available by digital technologies (Hellekson, Busse 2006; Stein, Busse 2012). In transmedia storytelling, integral elements of a story are dispersed systematically across various delivery channels with the goal of creating a networked entertainment experience (Jenkins 2006, 2007). Stories are no longer experienced through linear narratives, but they are accessed through several “points of entries” that encourage a customized reading/viewing/writing practice: the global fanfiction experience. Fanfiction is explored of this study from two distinct but complementary theoretical and methodological standpoints. Building on previous studies on the matter (Thomas 2010; Sindoni 2013) and drawing on a mono-generic corpus, LJFic, the research questions that this paper addresses deal with diatypic variation (Halliday 1991) in fanfiction from both linguistic and multimodal perspectives. Keyness analyses have been carried out using two different reference corpora (FLOB and COCA), assuming that such analyses can shed light on a range of linguistic issues, for example with regard to spoken/written variation (approximating Biber’s MF/MD analysis, 1988) and with a focus on the most prominent lexical items for the investigation of the entries’ aboutness (Scott, Tribble 2006; Bondi, Scott 2010). However, a purely computational analysis cannot account for the multimodal nature of fanfiction. To fill this gap, a sample of entries will be analysed qualitatively, by unearthing and unpacking the multimodal resources involved.

The distribution of L-motifs (measured on a text T) is similar to the L-motifs distribution measured on the pseudotext T’ constructed by random transposition of all tokens within the text T. This inspires the suggestion that the distribution of L-motifs is inherited from the word length distribution (or, by other words, that the word length distribution of a text implies the distribution of L-motifs). The paper clearly shows that despite of the similarity, an L-motifs structure, independent of the word length distribution, can be detected.

Common patterns of interactions are altered in the digital world and new patterns of communication have emerged, challenging previous notions of what communication actually is in the contemporary age. Online configurations of interaction, such as video chats, blogging, and social networking practices demand profound rethinking of the categories of linguistic analysis, given the blurring of traditional distinctions between oral and written discourse in digital texts. This volume reconsiders underlying linguistic and semiotic frameworks of analysis of spoken and written discourse in the light of the new paradigms of online communication, in keeping with a multimodal corpus linguistics theoretical framework.

Typical modes of online interaction encompass speech, writing, gesture, movement, gaze, and social distance. This is nothing new, but here Sindoni asserts that all these modes are integrated in unprecedented ways, enacting new interactional patterns and new systems of interpretation among web users. These "non verbal" modes have been sidelined by mainstream linguistics, whereas accounting for the complexity of new genres and making sense of their educational impact is high on this volume’ s agenda. Sindoni analyzes other new phenomena, ranging from the intimate sphere (i.e. video chats, personal blogs or journals on social networking websites) to the public arena (i.e. global-scale transmission of information and knowledge in public blogs or media-sharing communities), shedding light on the rapidly changing global web scenario.

""In this paper we examine the definitions of two widely-used interrelated constructs in corpus linguistics, keyness and keywords, as presented in the literature and corpus software manuals. In particular, we focus on
a. the consistency of definitions given in different sources;
b. the metrics used to calculate the level of keyness;
c. the compatibility between definitions and metrics.

Our survey of studies employing keyword analysis has indicated that the vast majority of studies examine a subset of keywords – almost always the top X number of keywords as ranked by the metric used. This renders the issue of the appropriate metric central to any study using keyword analysis.

In this study, we first argue that an appropriate, and therefore useful, metric for keyness needs to be fully consistent with the definition of keyword. We then use four sets of comparisons between corpora of different types and sizes, in order to test whether and to what extent the use of different metrics affects the ranking of keywords. More precisely, we look at the extent of overlap in the keyword rankings resulting from the adoption of different metrics, and we discuss the implications of ranking-based analysis adopting one metric or another. Finally, we propose a new metric for keyness, and demonstrate a simple way to calculate the metric, which supplements the keyword extraction in existing corpus software.""