scholarly journals Examining Users’ Concerns while Using Mobile Learning Apps

2021 ◽  
Vol 15 (15) ◽  
pp. 47
Author(s):  
Senanu Okuboyejo ◽  
Ooreofe Koyejo
Keyword(s):  
Mobile Learning ◽  
User Satisfaction ◽  
Information Overload ◽  
Online Reviews ◽  
Handheld Devices ◽  
Word Count ◽  
Significant Information ◽  
Linguistic Inquiry Word Count ◽  
Linguistic Inquiry ◽  
Massive Information

<p class="0abstract">Mobile learning applications (apps) are increasingly and widely adopted for learning purposes and educational content delivery globally, especially with the massive means of accessing the internet done majorly on mobile handheld devices. Users often submit their feedback on use, experience and general satisfaction via the reviews and ratings given in the digital distribution platforms. With this massive information given through the reviews, it presents an opportunity to derives valuable insights which can be utilized for various reasons and by different stakeholders of these mobile learning apps. This large volume of online reviews creates significant information overload which presents a time-consuming task to read through all reviews. By combining text mining techniques of topic modeling using Latent Dirichlet Algorithm (LDA) and sentiment analysis using Linguistic Inquiry Word Count (LIWC), we analyze these user reviews. These techniques identify inherent topics in the reviews and identifies variables of user satisfaction of mobile learning apps. The thematic analysis done reveals different keywords which guide classification into the topics identified. Conclusively, the topics derived are important to app stakeholders for further modifications and evolution tasks.</p>

scholarly journals Expressions of doubt and trust in online user reviews

2019 ◽  
Author(s):  
Anthony M Evans ◽  
Olga Stavrova ◽  
Hannes Rosenbusch
Keyword(s):  
Conflicts Of Interest ◽  
Online Reviews ◽  
Word Count ◽  
Linguistic Complexity ◽  
Psychological Variables ◽  
User Reviews ◽  
Linguistic Inquiry Word Count ◽  
Online User Reviews ◽  
Linguistic Inquiry

How do expressions of doubt affect trust in online reviews? Previous research leads to conflicting predictions: some studies find that people trust confident advisors more than doubtful advisors, whereas others find doubtful advisors are trusted more, especially when advisors have salient conflicts-of-interest. We tested the effects of doubt in the Yelp Open Dataset (N = 5.9 million user reviews). Reviews were coded using the Linguistic Inquiry Word Count (LIWC) software, which contains two dictionaries related to doubt, tentativeness and (lack of) certainty. Doubtful reviews were more likely to be seen as useful, and this result was robust when controlling for other psychological variables, as well as review length and linguistic complexity. The beneficial consequences of expressing doubt were strongest for positive (5-star) reviews, suggesting that doubt may mitigate concerns about the veracity of overly positive reviews. The present study emphasizes the advantages of expressing doubt.

Linguistic Markers in Women’s Discussions on Miscarriage and Abortion Illustrate Psychological Responses to Their Experiences

2020 ◽  
pp. 0261927X2096564
Author(s):  
Kate G. Blackburn ◽  
Weixi Wang ◽  
Rhea Pedler ◽  
Rachel Thompson ◽  
Diana Gonzales
Keyword(s):  
Emotion Regulation ◽  
Text Analysis ◽  
The Other ◽  
Psychological Responses ◽  
Word Count ◽  
Future Directions ◽  
Trauma Research ◽  
Linguistic Markers ◽  
Linguistic Inquiry Word Count ◽  
Linguistic Inquiry

This study analyzed thousands of women’s online conversations in relation to their miscarriage or abortion experiences, classified as unplanned and planned traumas, respectively. Linguistic Inquiry Word Count text analysis revealed that people experiencing a planned trauma use distancing language patterns in higher frequency and engage in emotion regulation more than those who experienced trauma unexpectedly. On the other hand, planned trauma conversations used more self-focused language and more social-based language. Implications and future directions for trauma research are discussed.

scholarly journals Analysing literacy and other psychological tendencies using linguistic profile in English expressive writing: Are students able but unwilling to write?

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Bonita Lee ◽  
Annisa Fitria ◽  
Henndy Ginting
Keyword(s):  
Critical Literacy ◽  
Expressive Writing ◽  
Business Students ◽  
Word Count ◽  
Significant Discrepancy ◽  
Men And Women ◽  
Global Competitiveness ◽  
Linguistic Inquiry Word Count ◽  
Linguistic Inquiry ◽  
Global Population

The use of English in educational settings has become quite common in order to achieve global competitiveness. Given this fact, students are required to be fluent both in oral and written English. Unfortunately, the significant discrepancy is often found between the two. Students seemed to struggle when asked to elaborate their ideas in writing. With that in mind, this study would elaborate on the linguistic properties of students’ writings in order to understand the linguistic processes affecting such a discrepancy. Writings from a total of 205-business students were analysed using Linguistic Inquiry Word Count (LIWC2015) focusing on the linguistic and grammatical properties such as word counts, tenses associated words, adjectives, adverbs and so on. We found that our samples’ writing profile was significantly different from those of LIWC2015, especially in properties such word counts, six-letter words, verb and adjectives, as well as the use of I-related pronoun. For example, we found that our sample used a lot more difficult words while wrote less than half of the global population, suggesting their ability as well as unwillingness to write at the same time. With this main finding, we concluded that students come short in terms of critical literacy. In addition to that, we would also discuss the potential psychological implications (narcissistic tendency) as well as the differences between men and women styles in writing.

Using LIWC to Analyze Participants’ Psychological Processing in Accounting JDM Research

2021 ◽  
Author(s):  
Sanaz Aghazadeh ◽  
Kris Hoang ◽  
Bradley Pomeroy
Keyword(s):  
Text Analysis ◽  
Affective States ◽  
Word Count ◽  
Psycholinguistic Research ◽  
Psychological Constructs ◽  
Written Responses ◽  
Linguistic Inquiry Word Count ◽  
Control Procedures ◽  
Linguistic Inquiry ◽  
Psychological Processing

This paper provides methodological guidance for judgment and decision-making (JDM) researchers in accounting who are interested in using the Linguistic Inquiry Word Count (LIWC) text analysis program to analyze research participants’ written responses to open-ended questions. We discuss how LIWC’s measures of psychological constructs were developed and validated in psycholinguistic research. We then use data from an audit JDM study to illustrate the use of LIWC to guide researchers in identifying suitable measures, performing quality control procedures, and reporting the analysis. We also discuss research design considerations that will strengthen the inferences drawn from LIWC analysis. The paper concludes with examples where LIWC analysis has the potential to reveal participants’ deep, complex, effortful psychological processing and affective states from their written responses.

A computer’s understanding of literature

2011 ◽  
Vol 1 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Arthur C. Graesser ◽  
Nia Dowell ◽  
Christian Moldovan
Keyword(s):  
Computational Linguistics ◽  
Discourse Processing ◽  
Literary Texts ◽  
Word Count ◽  
Social Scientists ◽  
Computer Tools ◽  
Linguistic Inquiry Word Count ◽  
Word Concreteness ◽  
Political Speeches ◽  
Linguistic Inquiry

Everyone agrees that a computer could never understand and appreciate literature, but the fields of computational linguistics and discourse processing have made important advances in automatic detection of language and discourse characteristics. We have analyzed literary texts and political speeches with two computer tools, namely Coh-Metrix and Linguistic Inquiry Word Count (LIWC). Coh-Metrix provides hundreds of measures that funnel into 5 principal components: word concreteness, syntactic simplicity, referential cohesion, deep cohesion, and narrativity. LIWC classifies words on 80 categories, such as first person pronouns, negative emotions, and social words. This paper illustrates how computer tools can unveil new insights about literature and can empirically test claims by literary scholars and social scientists. Our approach offers a computational science of literature.

Language-Style Similarity and Social Networks

2019 ◽  
Vol 31 (2) ◽  
pp. 202-213 ◽  
Author(s):  
Balazs Kovacs ◽  
Adam M. Kleinbaum
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Online Reviews ◽  
Word Count ◽  
Data Set ◽  
Friendship Formation ◽  
Linguistic Style ◽  
Large Scale Data ◽  
Linguistic Inquiry ◽  
Scale Data

This research demonstrates that linguistic similarity predicts network-tie formation and that friends exhibit linguistic convergence over time. In Study 1, we analyzed the linguistic styles and the emerging social network of a complete cohort of 285 students. In Study 2, we analyzed a large-scale data set of online reviews. In both studies, we collected data in two waves to examine changes in both social networks and linguistic styles. Using the Linguistic Inquiry and Word Count (LIWC) framework, we analyzed the text of students’ essays and of 1.7 million reviews by 159,651 Yelp reviewers. Consistent with our theory, results showed that similarity in linguistic style corresponded to a higher likelihood of friendship formation and persistence and that friendship ties, in turn, corresponded to a convergence in linguistic style. We discuss the implications of the coevolution of linguistic styles and social networks, which contribute to the formation of relational echo chambers.

The cultural influences of narrative content on consumers’ perceptions of helpfulness

2021 ◽  
pp. 147078532110230
Author(s):  
Ning Fu
Keyword(s):  
Rapid Development ◽  
Cultural Influences ◽  
Online Reviews ◽  
The United States ◽  
User Generated Content ◽  
Text Analytics ◽  
Word Count ◽  
Cultural Backgrounds ◽  
Linguistic Inquiry ◽  
The Impact

The rapid development of text analytics enables marketers to obtain the information extracted from the narrative content in user-generated content (UGC). Recent studies have also demonstrated that people with different cultural backgrounds may express their opinions about their purchase in diverse manners. This study focuses on the impact of the narrative content of consumers’ perception of helpfulness. It first identifies four contextual dimensions to propose a theoretical model, demonstrating that perceptions of helpfulness may differ in respect to the consumers’ varied cultural backgrounds (e.g., individualism vs. collectivism). By using Linguistic Inquiry and Word Count (LIWC), the study empirically tests the hypotheses by analyzing 111,857 movie reviews collected for 167 American movies released both in the United States and in China from 2013 to 2016. The results reveal that individualist consumers perceive an online review that contains more self-description and future-focus content as helpful, whereas collectivist consumers rely more on online reviews containing social description and past-focus content.

scholarly journals Twitter, time and emotions

2021 ◽  
Vol 8 (5) ◽  
pp. 201900
Author(s):  
Eric Mayor ◽  
Lucas M. Bietti
Keyword(s):  
Sentiment Analysis ◽  
Emotional Expression ◽  
Well Being ◽  
Temporal Variations ◽  
Word Count ◽  
Linguistic Inquiry Word Count ◽  
Sentiment Lexicon ◽  
Linguistic Inquiry ◽  
Expressed Emotions ◽  
Similarities And Differences

The study of temporal trajectories of emotions shared in tweets has shown that both positive and negative emotions follow nonlinear circadian (24 h) and circaseptan (7-day) patterns. But to this point, such findings could be instrument-dependent as they rely exclusively on coding using the Linguistic Inquiry Word Count. Further, research has shown that self-referential content has higher relevance and meaning for individuals, compared with other types of content. Investigating the specificity of self-referential material in temporal patterns of emotional expression in tweets is of interest, but current research is based upon generic textual productions. The temporal variations of emotions shared in tweets through emojis have not been compared to textual analyses to date. This study hence focuses on several comparisons: (i) between Self-referencing tweets versus Other topic tweets, (ii) between coding of textual productions versus coding of emojis, and finally (iii) between coding of textual productions using different sentiment analysis tools (the Linguistic Inquiry and Word Count—LIWC; the Valence Aware Dictionary and sEntiment Reasoner—VADER and the Hu Liu sentiment lexicon—Hu Liu). In a collection of more than 7 million Self-referencing and close to 18 million Other topic content-coded tweets, we identified that (i) similarities and differences in terms of shape and amplitude can be observed in temporal trajectories of expressed emotions between Self-referring and Other topic tweets, (ii) that all tools feature significant circadian and circaseptan patterns in both datasets but not always, and there is often a correspondence in the shape of circadian and circaseptan patterns, and finally (iii) that circadian and circaseptan patterns obtained from the coding of emotional expression in emojis sometimes depart from those of the textual analysis, indicating some complementarity in the use of both modes of expression. We discuss the implications of our findings from the perspective of the literature on emotions and well-being.

scholarly journals POLITICAL DISCOURSE CONTENT ANALYSIS: A CRITICAL OVERVIEW OF A COMPUTERIZED TEXT ANALYSIS PROGRAM LINGUISTIC INQUIRY AND WORD COUNT (LIWC)

2020 ◽  
Vol 1 (9(77)) ◽  
pp. 139-142
Author(s):  
Angelika Yanovets ◽  
Oksana Smal
Keyword(s):  
Content Analysis ◽  
Political Discourse ◽  
Analysis Program ◽  
Word Count ◽  
Linguistic Inquiry Word Count ◽  
Public Consciousness ◽  
Cognitive Information ◽  
Common Technique ◽  
Linguistic Inquiry ◽  
Speech Events

The article examines and analyzes the linguistic and psychological features of political discourse using a computer-based Linguistic Inquiry and Word Count (LIWC) content analysis program to explore the relationship between political discourse and the personality of politicians. As for political discourse, it is perhaps the communicator, the linguistic personality, who plays the most important role in the communication. The linguistic personality of a politician is of particular interest in political discourse content-analysis, since it has the greatest influence on the public consciousness via mass media. Using text as a source of psychological and cognitive information has been gaining popularity. Researchers use a variety of methods to analyze texts, but Linguistic Inquiry Word Count (LIWC) has proved to be the most common technique. The analysis of linguistic patterns of political discourse shows that in the context of political speech events such as media interviews, politicians make a unique choice of lexical units, which can be interpreted as a manifestation of certain personality traits. However, despite the significance of the results, there are clear limitations to the use of computerized methodologies to make political discourse content-analysis, such as the limited interpretive capacity of software to understand pragmatic and contextual use of lexical units.

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