More than the Quantity: The Value of Editorial Reviews for a User-Generated Content Platform

2021 ◽  
Author(s):  
Yipu Deng ◽  
Jinyang Zheng ◽  
Warut Khern-am-nuai ◽  
Karthik Kannan
Keyword(s):  
Language Processing ◽  
Quantitative Aspect ◽  
Qualitative Aspect ◽  
User Reviews ◽  
Main Driver ◽  
Long Time ◽  
Editorial Review ◽  
Processing Techniques ◽  
Professional Writers ◽  
Positive Effect

We investigate an editorial review program for which a review platform supplements user reviews with editorial ones written by professional writers. Specifically, we examine whether and how editorial reviews influence subsequent user reviews (reviews written by noneditor reviewers). A quasiexperiment conducted on a leading review platform in Asia, based on several econometric and natural language processing techniques, yields empirical evidence of an overall positive effect of editorial reviews on subsequent user reviews from the platform’s perspective. First, more reviews are provided for restaurants that receive editorial reviews. In addition, these reviews discuss substantive topics while also including a discussion on other topics, leading to a net increase in content length and variety. They also are more neutral in sentiment and are associated with lower rating valences. Further analysis of the mechanism reveals that the subsequent user reviews of the restaurants that receive editorial reviews become more similar to the editorial reviews in regard to topics, sentiment/rating, length, and readability, indicating a herding effect in how to write a review as the main driver of the change in the subsequent reviews. We further empirically isolate this herding effect among long-time reviewers. The findings suggest that review platforms could use an editorial review program not only to boost the quantitative aspect of user reviews but also, to manage the qualitative aspect as well. This paper was accepted by Kartik Hosanagar, information systems.

Using Self-report to Study How Application affect Motivation of Behavior Change (Preprint)

2020 ◽  
Author(s):  
Yuan Yin ◽  
Yurong Yu
Keyword(s):  
Behavior Change ◽  
Self Report ◽  
Group Method ◽  
Behavior Changes ◽  
Technology Design ◽  
Factors Affecting ◽  
Negative Results ◽  
Long Time ◽  
Ecological Momentary ◽  
Positive Effect

BACKGROUND Currently, changing behaviors with the assistance of mobile applications has been popularized. However, most of the participants are unable to persist in participating in behavior-changing activities for a long time. Some researchers have studied what factors motivate people to maintain behaviors-changing actions. There has been controversy about whether the commonly used triggers, negative results or competitions, could motivate behavior changes. In the meantime, the main methodology these researchers have been using is to conduct experiments, from which data was collected from subjects’ recalling previous behavior changing. The experiments are time-consuming, and the results can be unreliable. To resolve this problem, the Ecological Momentary Assessment (EMA) was developed to record real-time feedback. However, the EMA unavoidably increases the workload of the subjects. OBJECTIVE This study investigated the factors affecting behavior change, especially from the motivation aspect. Additionally, this paper attempted to identify a way to record human behavior changes without increasing the subjects’ workload. METHODS The methodology of “self-report” was adopted to report how people’s views regarding the behavior-changing intervention. To achieve a balance between workload and being timely, the self-reporting data was recorded once a day. After the 28-day “self-report” experiment, the “focus group” method was used to gather people’s feedback on behavior changing process. RESULTS This paper identified 9 factors: cooperation, competition, award, understandable graphic, reminder and alarm, trust and willing, gender, relation with disease and environmental factors). These factors could affect motivation of behavior changing. Besides, we found that negative results could be a motivation for behavior changing. In the experiment, we also found that a small number of subjects tended to cheat for a more “beautiful” result. The last part of the paper has presented possible implications for technology design to facilitate behavior-changing. CONCLUSIONS In particular, (i) the research promoted the possibility of cheating when recording data which is ignored by existing research and will make the digital applications less useful; (ii) the results show that not all cooperation is needed to lead to a positive effect; (iii) the research identified the negative results caused by over-competition in behavior change. Finally, the paper proposes technology design directions should focus on giving motivation through keeping dairy, negative results feedback and avoid cheating.

scholarly journals Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

scholarly journals Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 664
Author(s):  
Nikos Kanakaris ◽  
Nikolaos Giarelis ◽  
Ilias Siachos ◽  
Nikos Karacapilidis
Keyword(s):  
Language Processing ◽  
Scientific Knowledge ◽  
Link Prediction ◽  
Performance Metrics ◽  
Future Research ◽  
Knowledge Graph ◽  
Prediction Problem ◽  
Textual Information ◽  
Research Collaborations ◽  
Processing Techniques

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.

scholarly journals A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842110286
Author(s):  
Kylie L. Anglin ◽  
Vivian C. Wong ◽  
Arielle Boguslav
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Intervention Implementation ◽  
Proof Of Concept ◽  
Coaching Intervention ◽  
Processing Techniques ◽  
Teacher Coaching ◽  
The Impact

Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention.

Exploring Library Core Competencies: A Text Mining Study of American Library Association’s Job Advertisements From 2006 Through 2017

2021 ◽  
pp. 089443932110272
Author(s):  
Qinghong Yang ◽  
Zehong Shi ◽  
Yan Quan Liu
Keyword(s):  
Language Processing ◽  
Work Experience ◽  
Core Competencies ◽  
Core Competency ◽  
Analytical Investigation ◽  
Market Demand ◽  
American Library ◽  
Job Titles ◽  
Job Advertisements ◽  
Processing Techniques

Are core competency requirements for relevant positions in the library shifting? Applying natural language processing techniques to understand the current market demand for core competencies, this study explores job advertisements issued by the American Library Association (ALA) from 2006 to 2017. Research reveals that the job demand continues to rise at a rate of 13% (2006–2017) and that the requirements for work experience are substantially extended, diversity of job titles becomes prevalent, and rich service experience and continuous lifelong learning skills are becoming more and more predominant for librarians. This analytical investigation informs the emerging demands in the American job market debriefing the prioritization and reprioritization of the current core competency requirements for ALA librarians.

scholarly journals Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews

2020 ◽  
Vol 30 (1) ◽  
pp. 192-208 ◽  
Author(s):  
Hamza Aldabbas ◽  
Abdullah Bajahzar ◽  
Meshrif Alruily ◽  
Ali Adil Qureshi ◽  
Rana M. Amir Latif ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Language Processing ◽  
Mobile Application ◽  
Knowledge Engineering ◽  
Machine Learning Algorithms ◽  
Application Development ◽  
User Reviews ◽  
N Gram ◽  
Logistic Regression Algorithm ◽  
Google Play

Abstract To maintain the competitive edge and evaluating the needs of the quality app is in the mobile application market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interact and express their review, rate and share their feedback about applications. In this paper we have scrapped 506259 of user reviews and applications rate from Google Play Store from 14 different categories. The statistical information was measured in the results using different of common machine learning algorithms such as the Logistic Regression, Random Forest Classifier, and Multinomial Naïve Bayes. Different parameters including the accuracy, precision, recall, and F1 score were used to evaluate Bigram, Trigram, and N-gram, and the statistical result of these algorithms was compared. The analysis of each algorithm, one by one, is performed, and the result has been evaluated. It is concluded that logistic regression is the best algorithm for review analysis of the Google Play Store applications. The results have been checked scientifically, and it is found that the accuracy of the logistic regression algorithm for analyzing different reviews based on three classes, i.e., positive, negative, and neutral.

Compansion: From research prototype to practical integration

1998 ◽  
Vol 4 (1) ◽  
pp. 73-95 ◽  
Author(s):  
KATHLEEN F. MCCOY ◽  
CHRISTOPHER A. PENNINGTON ◽  
ARLENE LUBEROFF BADMAN
Keyword(s):  
Language Processing ◽  
Augmentative And Alternative Communication ◽  
Communication Aids ◽  
Alternative Communication ◽  
Communicative Ability ◽  
Linguistic Ability ◽  
Research Prototype ◽  
Intelligent Communication ◽  
Processing Techniques ◽  
Parser Generator

Augmentative and Alternative Communication (AAC) is the field of study concerned with providing devices and techniques to augment the communicative ability of a person whose disability makes it difficult to speak or otherwise communicate in an understandable fashion. For several years, we have been applying natural language processing techniques to the field of AAC to develop intelligent communication aids that attempt to provide linguistically correct output while increasing communication rate. Previous effort has resulted in a research prototype called Compansion that expands telegraphic input. In this paper we describe that research prototype and introduce the Intelligent Parser Generator (IPG). IPG is intended to be a practical embodiment of the research prototype aimed at a group of users who have cognitive impairments that affect their linguistic ability. We describe both the theoretical underpinnings of Compansion and the practical considerations in developing a usable system for this population of users.

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