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The purpose of this study was to investigate the effects of word-related factors (i.e. frequency, range, dispersion and cognateness) on first-language English (L1E) readers' (n = 20) and second-language English (L2E) readers' (n = 20) incidental acquisition of vocabulary through the reading of an authentic novel.

Participants read A Clockwork Orange by Anthony Burgess, a 58,686 token (word) English language novel containing Slovos, that is, words from Nadsat, a futuristic, foreignized teen talk invented by Burgess. Upon finishing the novel, the participants took two unexpected vocabulary tests, one for meaning recognition and the other for meaning recall.

The results of this study indicate that word-related factors significantly correlate with the word meaning recall test scores of both groups. However, the regression models of meaning recall for the two groups showed that dispersion was the most robust predictor, which implies that the participants recalled more word meanings when the novel had a more even distribution of the unknown target words. The meaning recognition test scores showed cognates were a significant predictor for the L1E readers but not for L2E readers.

This study marks the first attempt in the field to investigate the relative contribution of frequency, range and dispersion – a closely bound set of word-related factors – to both L1E and L2E readers' incidental acquisition of vocabulary through reading an authentic novel. Considering the important role of dispersion, the current study suggests that developers of graded readers and children's literature should more evenly distribute unknown target words in their books. Doing so will better facilitate both L1E and L2E readers' acquisition of those words. The study also addresses a fallacy of methodology regarding incidental vocabulary acquisition by examining the effect of the cognateness of the foreignized words embedded in A Clockwork Orange. The L1E readers' sensitivity to cognates implies that cognate-word awareness-raising activities are necessary to learning a foreign language, especially if that language has many cognates in common with English, such as Spanish.

The purpose of this chapter is to present key characteristics of early reading text by describing a new series of researcher-developed early reading books that were specifically designed to address multiple criteria, including word structure or decodability, familiarity, repetition, high-frequency, syntax, and text cohesion. We describe the theoretical and empirical rationale that guided the design of the books, how we developed them, and their key features. This is followed by a technical analysis that describes the (1) characteristics of the target words used to guide the writing of the books and (2) characteristics of the text, such as the percentage of words on common high-frequency word lists, word counts, type-token ratio, sentence counts, unique sight words, unique decodable words, and content (i.e., picture-supported) words. The analysis demonstrates that the target words and the text in the books are consistent with our intended goal of simultaneously addressing multiple variables.

Sentiment lexicon is an essential resource for sentiment analysis of user reviews. By far, there is still a lack of domain sentiment lexicon with large scale and high accuracy for Chinese book reviews. This paper aims to construct a large-scale sentiment lexicon based on the ultrashort reviews of Chinese books.

First, large-scale ultrashort reviews of Chinese books, whose length is no more than six Chinese characters, are collected and preprocessed as candidate sentiment words. Second, non-sentiment words are filtered out through certain rules, such as part of speech rules, context rules, feature word rules and user behaviour rules. Third, the relative frequency is used to select and judge the polarity of sentiment words. Finally, the performance of the sentiment lexicon is evaluated through experiments.

This paper proposes a method of sentiment lexicon construction based on ultrashort reviews and successfully builds one for Chinese books with nearly 40,000 words based on the Douban book.

Compared with the idea of constructing a sentiment lexicon based on a small number of reviews, the proposed method can give full play to the advantages of data scale to build a corpus. Moreover, different from the computer segmentation method, this method helps to avoid the problems caused by immature segmentation technology and an imperfect N-gram language model.

The purpose of this study is to introduce the homophone emotional interest superiority effect in phonological, or sound-based, priming, whereby pseudohomophone brand names (i.e. non-words that are pronounced identically to English words, for example, Bie) prime brand meaning associated with the member of the homophone pair that is emotionally interesting (i.e. Bie will be prime brand avoidance (purchase) when consumers are emotionally interested in the homophone bye [buy]).

Studies 1 and 2 examine the effect of homophone emotional interest on brand judgements and behaviours. Study 3 investigates the role of boredom with the brand name in attenuating the homophone emotional interest superiority effect.

Findings indicate that pseudohomophone brand names prime brand judgements and behaviours associated with the word from the homophone pair that evokes emotional interest. Study 2 provides further evidence of homophone emotional interest as the process influencing brand judgements and behaviours. Study 3 establishes that the effect of pseudohomophone brand names on brand judgements weaken when boredom with the brand name is induced.

This study is limited, as it focuses only on fictitious brands and methodologically creates boredom in a way in which may not be typical of what would be experienced in the real world.

This study has important implications for brand managers in the development of new brand names and in prioritising the intended homophone pair from a pseudohomophone brand name to influence consumer judgements and behaviours.

This study introduces and provides evidence of a homophone emotional interest superiority effect. This study also identifies a condition under which the homophone emotional interest superiority effect is attenuated.

This study aims to investigate the effects of word internal morphological form variation on adult first language (L1) (n = 20) incidental vocabulary acquisition through reading.

Participants were given a 37,611-token English novel containing pseudo words, placed throughout the text by the novelist. Two unexpected vocabulary assessments were administered at the completion of the reading task.

Results showed statistically significant effects for morphological form variation, with the readers having incidentally acquired more words whose tokens did not vary in form (i.e. no exposure to inflectional or derivational variants). However, a large effect size was present only for low-frequency words (two-four exposures).

Discussion of the results is given regarding the feasibility of enhancing adult L1 college readers’ morphological awareness through extensive reading and attention-drawing tasks.

Four term‐weighting schemes are used to detect information‐rich passages in texts and the results are compared. It is demonstrated that word categories and frequency‐derived weights have a close correlation but that weighting according to the first mention theory or the cue‐method shows no correlation with frequency‐based weights.

When a large number of project proposals are evaluated to allocate available funds, grouping them based on their similarities is beneficial. Current approaches to group proposals are primarily based on manual matching of similar topics, discipline areas and keywords declared by project applicants. When the number of proposals increases, this task becomes complex and requires excessive time. This paper aims to demonstrate how to effectively use the rich information in the titles and abstracts of Turkish project proposals to group them automatically.

This study proposes a model that effectively groups Turkish project proposals by combining word embedding, clustering and classification techniques. The proposed model uses FastText, BERT and term frequency/inverse document frequency (TF/IDF) word-embedding techniques to extract terms from the titles and abstracts of project proposals in Turkish. The extracted terms were grouped using both the clustering and classification techniques. Natural groups contained within the corpus were discovered using k-means, k-means++, k-medoids and agglomerative clustering algorithms. Additionally, this study employs classification approaches to predict the target class for each document in the corpus. To classify project proposals, various classifiers, including k-nearest neighbors (KNN), support vector machines (SVM), artificial neural networks (ANN), classification and regression trees (CART) and random forest (RF), are used. Empirical experiments were conducted to validate the effectiveness of the proposed method by using real data from the Istanbul Development Agency.

The results show that the generated word embeddings can effectively represent proposal texts as vectors, and can be used as inputs for clustering or classification algorithms. Using clustering algorithms, the document corpus is divided into five groups. In addition, the results demonstrate that the proposals can easily be categorized into predefined categories using classification algorithms. SVM-Linear achieved the highest prediction accuracy (89.2%) with the FastText word embedding method. A comparison of manual grouping with automatic classification and clustering results revealed that both classification and clustering techniques have a high success rate.

The proposed model automatically benefits from the rich information in project proposals and significantly reduces numerous time-consuming tasks that managers must perform manually. Thus, it eliminates the drawbacks of the current manual methods and yields significantly more accurate results. In the future, additional experiments should be conducted to validate the proposed method using data from other funding organizations.

This study presents the application of word embedding methods to effectively use the rich information in the titles and abstracts of Turkish project proposals. Existing research studies focus on the automatic grouping of proposals; traditional frequency-based word embedding methods are used for feature extraction methods to represent project proposals. Unlike previous research, this study employs two outperforming neural network-based textual feature extraction techniques to obtain terms representing the proposals: BERT as a contextual word embedding method and FastText as a static word embedding method. Moreover, to the best of our knowledge, there has been no research conducted on the grouping of project proposals in Turkish.

Excavating valuable outlier information of gray privacy products, the purpose of this study takes the online reviews of women’s underwear as an example, explores the outlier characteristics of online commentary data, and analyzes the online consumer behavior of consumers’ gray privacy products.

This research adopts the social network analysis method to analyze online reviews. Based on the online reviews collected from women’s underwear flagship store Victoria’s Secret at Tmall, this study performs word segmentation and word frequency analysis. Using the fuzzy query method, the research builds the corresponding co-word matrix and conducts co-occurrence analysis to summarize the factors affecting consumers’ purchase behavior of female underwear.

Establishing a formal framework of gray privacy products, this paper confirms the commonalities among consumers with respect to their perceptions of gray privacy products, shows that consumers have high privacy concerns about the disclosure or secondary use of personal private information when shopping gray privacy products, and demonstrates the big difference between online reviews of gray privacy products and their consumer descriptions.

The research lays a solid foundation for future research in gray privacy products. The factors identified in this study provide a practical reference for the continuous improvement of gray privacy products and services.

A growing body of research outlines how to best facilitate and ensure methodological rigor when using dictionary-based computerized text analyses (DBCTA) in organizational research. However, these best practices are currently scattered across several methodological and empirical manuscripts, making it difficult for scholars new to the technique to implement DBCTA in their own research. To better equip researchers looking to leverage this technique, this methodological report consolidates current best practices for applying DBCTA into a single, practical guide. In doing so, we provide direction regarding how to make key design decisions and identify valuable resources to help researchers from the beginning of the research process through final publication. Consequently, we advance DBCTA methods research by providing a one-stop reference for novices and experts alike concerning current best practices and available resources.