Textual data require an analytical trade-off between breadth and depth. Automated approaches locate patterns across large swaths of data points but sacrifice qualitative insight because they are not ...well equipped to deal with context-determined ways to express meaning, like figurative language. To strengthen the power of automated text analysis, researchers seek hybrid methodologies that combine computer-augmented analysis with sociocultural researcher insights based on qualitative textual interpretation. This article demonstrates a new method, which the authors term metaphor-enabled marketplace sentiment analysis (MEMSA). Building on existing automated text analysis methodologies linking word lists to sentiments, MEMSA adds metaphors that associate topics with sentiments across domains. Using MEMSA, researchers can leverage the sentiment potential of these located metaphors and scale insights to the level of big textual data by employing a dictionary approach enhanced by a specific and useful linguistic property of metaphors: their predictable structure in text (something is something else). This article shows that metaphors add associative detail to sentiments, revealing the targets and sources of sentiments that underlie the associations. Understanding nuanced market sentiments enables marketers to identify sentiment-based trends embedded in market discourse, so they can better formulate, target, position, and communicate value propositions for products and services.
In recent years, dockless bike-sharing programs have been introduced to either substitute or complement docked bike-sharing programs. Riders of these devices always have perceived differences of one ...system over the other, which could vary across gender. This study applied a text network approach to explore the residents' perceptions of the dockless bike-sharing program across gender. The study used over 700 responses collected between February and March 2018 in Seattle, Washington. The results revealed that ease of use, convenience, safety, pricing, and quality areas make a tremendous difference in the perception of dockless over docked bike-sharing systems. The perception of ease of use and convenience does not vary significantly across genders. On the other hand, male respondents were more aligned on the better pricing scheme and the bikes' quality than female respondents. Conversely, female respondents did care more about safety in terms of helmet use. Moreover, female respondents were more explicit in explaining the negative characteristics of the dockless bike-sharing system over docked ones. Study findings can help policymakers and operators of dockless bikes to provide equity in service for both genders.
•We explored perceptions of Seattle residents on the dockless bike-sharing system across gender.•We applied a text mining approach to data collected between February and March 2018.•We found that perception of ease of use and convenience does not vary significantly across genders.•Conversely, male respondents cared much about pricing while females focused on safety.•The findings are important for the operators to improve operations.
The explosive growth of text data requires effective methods to represent and classify these texts. Many text learning methods have been proposed, like statistics-based methods, semantic similarity ...methods, and deep learning methods. The statistics-based methods focus on comparing the substructure of text, which ignores the semantic similarity between different words. Semantic similarity methods learn a text representation by training word embedding and representing text as the average vector of all words. However, these methods cannot capture the topic diversity of words and texts clearly. Recently, deep learning methods such as CNNs and RNNs have been studied. However, the vanishing gradient problem and time complexity for parameter selection limit their applications. In this paper, we propose a novel and efficient text learning framework, named Latent Topic Text Representation Learning . Our method aims to provide an effective text representation and text measurement with latent topics. With the assumption that words on the same topic follow a Gaussian distribution, texts are represented as a mixture of topics, i.e., a Gaussian mixture model. Our framework is able to effectively measure text distance to perform text categorization tasks by leveraging statistical manifolds. Experimental results on text representation and classification, and topic coherence demonstrate the effectiveness of the proposed method.
The current digital era offers many possibilities to modify the layout of a text to optimize reading and improve comprehension. Here, we examined the idea that the visuo-spatial properties of ...segmented layouts support beginning readers by reducing the demands of basic eye-movement processes. In a series of self-paced reading experiments, text comprehension and reading speed of second- and third-grade pupils (N = 348) were assessed in a baseline condition (i.e., sentences continued on the same line as far as page width allowed) and three conditions with a segmented layout: (1) a discontinuous layout in which each sentence was presented on a new line of the page; (2) a reader-paced Rapid Serial Visual Presentation (RSVP) layout in which the texts were presented sentence by sentence; (3) a reader-paced RSVP layout in which the texts were presented word by word. No advantages were observed for the discontinuous layout. However, at the expense of increased reading times, robust comprehension advantages emerged for the two RSVP layouts. The observed trade-off between speed and accuracy suggests that a RSVP-based layout induces more precise reading, rather than reducing the demands on basic decoding and oculomotor control processes. These findings will be discussed in the context of individual differences in reading skills and several high-potential digital applications that aim at enhancing the abilities of (beginning) readers (e.g., Spritz, BeeLine Reader).
•In a segmented layout, texts are presented chunk by chunk.•Pupils in Grades 2 and 3 (7–9 years old) benefit from a segmented layout.•Segmented texts probe higher-order comprehension by inducing more accurate reading.•Reading skills and text genre influence the efficacy of the layout of a text.•Segmented texts are particularly useful in the early stages of reading acquisition.
The article is devoted to the scientific school developed by the first author in 1995–2012 in Yaroslav-the-Wise Novgorod State University (Veliky Novgorod, Russia). The finite practical goal of the ...research carried out by the school can be denoted here as the revelation of the most rational variant for sense transfer in a knowledge unit defined by a set of semantically equivalent natural-language phrases. One phrase here corresponds to the simple spread natural-language sentence (according to the “Meaning–Text” theory terminology). Knowledge formed herewith about synonymy and forms of language expression of relationships between concepts of some topical area are in demand in tasks requiring the establishment of full or partial equivalence in the meaning of both complete sentences of natural language and their combinations, and individual fragments of phrases. The results are both theoretical and practical in nature. Offered methods and their software implementations can be used for decision of a wide range of tasks of recognition and analysis of semantics of complex information objects (texts and images at first), and for lossless-in-sense information compression.
The problem of cross-modal retrieval from multimedia repositories is considered. This problem addresses the design of retrieval systems that support queries across content modalities, for example, ...using an image to search for texts. A mathematical formulation is proposed, equating the design of cross-modal retrieval systems to that of isomorphic feature spaces for different content modalities. Two hypotheses are then investigated regarding the fundamental attributes of these spaces. The first is that low-level cross-modal correlations should be accounted for. The second is that the space should enable semantic abstraction. Three new solutions to the cross-modal retrieval problem are then derived from these hypotheses: correlation matching (CM), an unsupervised method which models cross-modal correlations, semantic matching (SM), a supervised technique that relies on semantic representation, and semantic correlation matching (SCM), which combines both. An extensive evaluation of retrieval performance is conducted to test the validity of the hypotheses. All approaches are shown successful for text retrieval in response to image queries and vice versa. It is concluded that both hypotheses hold, in a complementary form, although evidence in favor of the abstraction hypothesis is stronger than that for correlation.
GeShort is a novel method for one-handed text editing and formatting on mobile devices. It uses simple rules to facilitate direct cursor positioning, gestural shortcuts inspired by keyboard hotkeys ...for editing and formatting, and a floating clipboard to enable delayed, repeated, and block editing. A comparison between GeShort and the default Google keyboard revealed that users perform editing and formatting tasks about 11% and 22% faster, respectively, with GeShort. This is achieved by significantly reducing selection time by 11% and action time by 17%. A second study comparing the clipboard features of the two methods revealed that users perform advanced editing tasks 34% faster with GeShort. Besides, participants find GeShort less onerous in mental demand, physical demand, and effort, which likely contribute to the overall performance gain. They also perceive GeShort as faster and easier to use, feel that its functions are better integrated, thus want to keep using it on their devices.
Mobile word suggestions can slow down typing, yet are still widely used. To investigate the apparent benefits beyond speed, we analyzed typing behavior of 15,162 users of mobile devices. Controlling ...for natural typing speed (a confounding factor not considered by prior work), we statistically show that slower typists use suggestions more often but are slowed down by doing so. To better understand how these typists leverage suggestions -- if not to improve their speed -- we extract eight usage strategies, including completion, correction, and next-word prediction. We find that word characteristics, such as length or frequency, along with the strategy, are predictive of whether a user will select a suggestion. We show how to operationalize our findings by building and evaluating a predictive model of suggestion selection. Such a model could be used to augment existing suggestion algorithms to consider people's strategic use of word predictions beyond speed and keystroke savings.
Purpose
– The purpose of this paper is to provide a holistic picture of how and to what extent Cohen and Levinthal’s (1990) seminal article on absorptive capacity was used in knowledge management ...(KM) and intellectual capital (IC) research from 1990 to 2013.
Design/methodology/approach
– In this paper, 186 articles extracted from eight KM and IC journals were reviewed by conducting both content and text analyses. To facilitate research comparison, content analysis followed the method used by Roberts et al. (2012) and thus was based on categories, conceptualizations, levels of analysis and, additionally, temporal evolution of absorptive capacity from 1990 to 2013 was looked at. Text analysis was performed to identify major research themes developing the absorptive capacity construct.
Findings
– Finding showed that absorptive capacity was largely underdeveloped in the KM and IC fields. KM, knowledge transfer and innovation were the top three research areas investigating absorptive capacity in the KM and IC fields.
Research limitations/implications
– This study had limitations related to time frame, covering a period from April 1990 to November 2013, and accessibility of articles due to specific restrictions in journal subscriptions.
Originality/value
– This paper is a first attempt to review absorptive capacity in KM and IC research. It represented a primary reference for those interested to research absorptive capacity in the KM and IC fields.
As the huge dimensionality of textual data restrains the classification accuracy, it is essential to apply feature selection (FS) methods as dimension reduction step in text classification (TC) ...domain. Most of the FS methods for TC contain several number of probabilities. In this study, we proposed a new FS method named as Extensive Feature Selector (EFS), which benefits from corpus-based and class-based probabilities in its calculations. The performance of EFS is compared with nine well-known FS methods, namely, Chi-Squared (CHI2), Class Discriminating Measure (CDM), Discriminative Power Measure (DPM), Odds Ratio (OR), Distinguishing Feature Selector (DFS), Comprehensively Measure Feature Selection (CMFS), Discriminative Feature Selection (DFSS), Normalised Difference Measure (NDM) and Max–Min Ratio (MMR) using Multinomial Naive Bayes (MNB), Support-Vector Machines (SVMs) and k-Nearest Neighbour (KNN) classifiers on four benchmark data sets. These data sets are Reuters-21578, 20-Newsgroup, Mini 20-Newsgroup and Polarity. The experiments were carried out for six different feature sizes which are 10, 30, 50, 100, 300 and 500. Experimental results show that the performance of EFS method is more successful than the other nine methods in most cases according to micro-F1 and macro-F1 scores.
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