Classifying Electronic Word of Mouth and Competitive Position in Online Game Industry

Manuel, Bram; Tricahyono, Dodie

doi:10.21108/jdsa.2018.1.9

Cited by 5 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…With the increase in online game availability, there is also significant growth in consumer reviews of those products (Manuel and Tricahyono, 2018). WOM communication allows players to share their feelings and excitement with others, potentially inviting new players to the game (Huang et al , 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Exploratory study on the perceptions of event gamification on positive behavioral outcomes

Sisson

Whalen

2021

JHTI

View full text Add to dashboard Cite

PurposeThe value of the events industry is increasing worldwide. An essential component for successful events is creating a unique experience by offering gamification. A conceptual model is proposed, exploring pre-event game communication and its effect on attendees' value perceptions, willingness to participate, word of mouth intentions and emotional commitment during gameplay at conference events.Design/methodology/approachAnalysis of variance and structural equation modeling were employed to test the model using data collected from 177 attendees recruited from hospitality and tourism association network listservs and online research company.FindingsResults reveal that perceptions of event gamification increase word of mouth intentions, willingness to participate and emotional commitment.Practical implicationsThe study contributes to the knowledge of conference events with recommendations for incorporation of game elements for meeting planners to enhance attendee behaviors at the event.Originality/valueThis study is among the first to examine positive behavioral outcomes of using games at conference events.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Exploratory study on the perceptions of event gamification on positive behavioral outcomes

Sisson

Whalen

2021

JHTI

View full text Add to dashboard Cite

show abstract

“…Terdapat bermacam-macam data yang digunakan dalam implementasi dalam penyelesaian task terhadap analisis sentiment tersebut. Macam-macam data yang digunakan adalah movie review [4], teks hadits [5,6], dan bahkan komentar di sosial media [7,8].…”

Section: Pendahuluanunclassified

Analisis Sentimen Review Film Berbahasa Inggris Dengan Pendekatan Bidirectional Encoder Representations from Transformers

Putri¹

2020

JATISI

View full text Add to dashboard Cite

Sentimen Analisis adalah proses analisis terhadap suatu pendapat atau sikap seseorang. Sentimen analisis digunakan untuk mendapatkan suatu hasil analisa terhadap berbagai macam pendapat atau sikap seseorang dalam memberikan komentar atau opininya. Pada penelitian ini, penulis melakukan klasifikasi sentiment analysis terhadap review film dengan menggunakan Dataset cornell edu dari pabo untuk movie review dengan proses klasifikasi menggunakan algoritma Bidirectional Encoder Representations from Transformers (BERT) yang dilakukan fine tuning dengan beberapa layer untuk klasifikasi. Dari penelitian ini, didapatkan hasil akurasi yang dihitung dengan sparse categorical cross entropy sebesar 73%.

show abstract

“…In this study, the algorithm used for classification is Naïve Bayes Classifier. When viewed by its complexity, Naïve Bayes Classifier is simpler and conventional than other algorithms [13]. So that the computational time needed in the classification process will certainly be shorter.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Cyberbullying on Instagram User Comments

Naf’an¹,

Bimantara²,

Larasati³

et al. 2019

J. Data Sci. Appl.

View full text Add to dashboard Cite

Instagram is a social media for sharing images, photos and videos. Instagram has many active users from various circles. In addition to sharing submissions, Instagram users can also give likes and comments to other users' posts. However, the comment feature is often misused, for example it is used for cyberbullying which includes one act against the law. But until now, Instagram still does not provide a feature to detect cyberbullying. Therefore, this study aims to create a system that can classify comments whether they contain elements of cyberbullying or not. The results of the classification will be used to detect cyberbullying comments. The algorithm used for classification is Naïve Bayes Classifier. Then for each comment will pass the preprocessing and feature extraction stages with the TF-IDF method. For evaluation and testing using the K-Fold Cross Validation method. The experiment is divided into two, namely using stemming and without stemming. The training data used is 455 data. The best experimental results obtained an accuracy of 84% both with stemming, and without stemming.

show abstract

Classifying Electronic Word of Mouth and Competitive Position in Online Game Industry

Cited by 5 publications

References 14 publications

Exploratory study on the perceptions of event gamification on positive behavioral outcomes

Exploratory study on the perceptions of event gamification on positive behavioral outcomes

Analisis Sentimen Review Film Berbahasa Inggris Dengan Pendekatan Bidirectional Encoder Representations from Transformers

Sentiment Analysis of Cyberbullying on Instagram User Comments

Contact Info

Product

Resources

About