Classification of User Comments for Social Media Applications using Text Mining

2019 ◽  
Author(s):  
Emre Dandıl ◽  
Burhan Karakurt
Keyword(s):  
Social Media ◽  
Text Mining ◽  
User Comments ◽  
Media Applications

scholarly journals “You Go, Girl:” How Facebook and Instagram impacted the post-feminist construction of Electric Forest’s Women’s Program

2021 ◽  
Author(s):  
Rebecca Krauss
Keyword(s):  
Social Media ◽  
Qualitative Analysis ◽  
Electronic Music ◽  
Past Research ◽  
Music Festival ◽  
User Comments ◽  
Media Applications ◽  
The Way

In February of 2016, Electric Forest — a four-day electronic music festival from June 23-26 in Rothbury, Michigan —announced a women’s only program called Her Forest. The initiative’s aim was to facilitate feelings of “connection, inspiration, and comfort” (Weiner, 2016) amongst the festival’s female guests. This MRP draws from past research on influence and postfeminism to consider how the Electric Forest brand, as well as its online followers, constructed and discussed Her Forest via Facebook and Instagram. A directed qualitative analysis was applied to 21 of Electric Forest’s Facebook and Instagram posts and 110 associated user comments. The analysis emphasized the powerful impact that social media applications have on the way in which corporate messages are expressed, received, reshaped, supported, and challenged.

Ooredoo Rayek

2020 ◽  
Vol 11 (2) ◽  
pp. 66-81
Author(s):  
Badia Klouche ◽  
Sidi Mohamed Benslimane ◽  
Sakina Rim Bennabi
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Experimental Results ◽  
Support Vector ◽  
Textual Data ◽  
New Strategy ◽  
Set Up

Sentiment analysis is one of the recent areas of emerging research in the classification of sentiment polarity and text mining, particularly with the considerable number of opinions available on social media. The Algerian Operator Telephone Ooredoo, as other operators, deploys in its new strategy to conquer new customers, by exploiting their opinions through a sentiments analysis. The purpose of this work is to set up a system called “Ooredoo Rayek”, whose objective is to collect, transliterate, translate and classify the textual data expressed by the Ooredoo operator's customers. This article developed a set of rules allowing the transliteration from Algerian Arabizi to Algerian dialect. Furthermore, the authors used Naïve Bayes (NB) and (Support Vector Machine) SVM classifiers to assign polarity tags to Facebook comments from the official pages of Ooredoo written in multilingual and multi-dialect context. Experimental results show that the system obtains good performance with 83% of accuracy.

Study of Potential Classification of Lost Students in College Based on Information Extraction on Text-Based Social Media; Case Study of Panca Budi Pembangunan University

2021 ◽  
Vol 8 (11) ◽  
pp. 325-331
Author(s):  
Eko Hariyanto ◽  
Sri Wahyuni ◽  
Supina Batubara
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Text Mining ◽  
Information Extraction ◽  
Preventive Measure ◽  
Drop Out ◽  
Training Data ◽  
Computational Algorithms ◽  
Technological Readiness

The main problem studied in this study is the large number of lost students who harm universities because of the difficulty of monitoring or monitoring as a preventive measure. Therefore, this research becomes very important to be done so that college institutions can make efforts to detect early (classification) of students who potentially cannot complete their studies on time or students who will drop out (DO). Thus, PT institutions through related parties such as academic guidance lecturers, academic bureaus and others can do initial prevention by providing the best solution or solution to the problems faced by students. This research aims to determine the training data model consisting of academic and non-academic factors (including the results of extracting information from social media). Furthermore, this model is used as a basis for classifying students who have the potential to "graduate on time", "graduate not on time", and "DO". The method approach used is quantitative with text mining computational algorithms for the process of extracting knowledge / information from social media which is further used in data training, as well as data mining computational algorithms for the process of classification of potential completion of student studies. The mandatory external targeted in the first year is the publication of the international journal Scopus Q4 and in the second year is the publication of the international journal Scopus Q3. For additional external targets in the first and second years respectively are the publication of international journals indexed on reputable indexers, ISBN teaching books and copyrights. The level of technological readiness (TKT) in this study up to level 2 is the formulation of technological concepts and applications to classify the potential completion of student studies using data mining. Keywords: [student lost, knowledge/information extraction, data classification, text mining, data mining].

scholarly journals Classification of User Comment Using Word2vec and Deep Learning

2021 ◽  
Vol 11 (5) ◽  
pp. 1-8
Author(s):  
Rafly Indra Kurnia ◽  
◽  
Abba Suganda Girsang
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
User Satisfaction ◽  
Rating Scale ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Training Data ◽  
Deep Learning Algorithm ◽  
User Comments

This study will classify the text based on the rating of the provider application on the Google Play Store. This research is classification of user comments using Word2vec and the deep learning algorithm in this case is Long Short Term Memory (LSTM) based on the rating given with a rating scale of 1-5 with a detailed rating 1 is the lowest and rating 5 is the highest data and a rating scale of 1-3 with a detailed rating, 1 as a negative is a combination of ratings 1 and 2, rating 2 as a neutral is rating 3, and rating 3 as a positive is a combination of ratings 4 and 5 to get sentiment from users using SMOTE oversampling to handle the imbalance data. The data used are 16369 data. The training data and the testing data will be taken from user comments MyTelkomsel’s application from the play.google.com site where each comment has a rating in Indonesian Language. This review data will be very useful for companies to make business decisions. This data can be obtained from social media, but social media does not provide a rating feature for every user comment. This research goal is that data from social media such as Twitter or Facebook can also quickly find out the total of the user satisfaction based from the rating from the comment given. The best f1 scores and precisions obtained using 5 classes with LSTM and SMOTE were 0.62 and 0.70 and the best f1 scores and precisions obtained using 3 classes with LSTM and SMOTE were 0.86 and 0.87

scholarly journals Text Mining to Understand the Influence of Social Media Applications on Smartphone Supply Chain

2018 ◽  
Vol 140 ◽  
pp. 87-94 ◽  
Author(s):  
Aditya Akundi ◽  
Bill Tseng ◽  
Jiamin Wu ◽  
Eric Smith ◽  
M Subbalakshmi ◽  
...  
Keyword(s):  
Social Media ◽  
Supply Chain ◽  
Text Mining ◽  
Media Applications

scholarly journals “You Go, Girl:” How Facebook and Instagram impacted the post-feminist construction of Electric Forest’s Women’s Program

2021 ◽  
Author(s):  
Rebecca Krauss
Keyword(s):  
Social Media ◽  
Qualitative Analysis ◽  
Electronic Music ◽  
Past Research ◽  
Music Festival ◽  
User Comments ◽  
Media Applications ◽  
The Way

In February of 2016, Electric Forest — a four-day electronic music festival from June 23-26 in Rothbury, Michigan —announced a women’s only program called Her Forest. The initiative’s aim was to facilitate feelings of “connection, inspiration, and comfort” (Weiner, 2016) amongst the festival’s female guests. This MRP draws from past research on influence and postfeminism to consider how the Electric Forest brand, as well as its online followers, constructed and discussed Her Forest via Facebook and Instagram. A directed qualitative analysis was applied to 21 of Electric Forest’s Facebook and Instagram posts and 110 associated user comments. The analysis emphasized the powerful impact that social media applications have on the way in which corporate messages are expressed, received, reshaped, supported, and challenged.

Playing videogames or using social media applications on smartphones causes mental fatigue and impairs decision-making performance in amateur boxers

2021 ◽  
pp. 1-12
Author(s):  
Leonardo Sousa Fortes ◽  
Petrus Gantois ◽  
Dalton de Lima-Júnior ◽  
Bruno Teixeira Barbosa ◽  
Maria Elisa Caputo Ferreira ◽  
...  
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Mental Fatigue ◽  
Media Applications
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