scholarly journals Anglicism in Indonesian

2018 ◽  
Vol 5 (1) ◽  
pp. 61-71
Author(s):  
Nurul Azizah
Keyword(s):  
Social Media ◽  
Semantic Analysis ◽  
Loan Words ◽  
Oral Form ◽  
Phonological Rules

This article discusses a language phenomenon currently occurring in Indonesia which is related to borrowing English words with the addition of a prefix ng-/nge in the Indonesian. The purpose of this article is to show how some English words are borrowed in Indonesian and what changes occur within this borrowing process which will be seen on two linguistic levels (phonological and semantic). The data were collected through an observation either in writing forms found in social media or oral form used in daily conversations. The interim results show that phonologically, in general the loan words follow the Indonesian phonological rules with little divergence in certain cases. From the semantic analysis it was found that these Anglicism words can be divided into three categories based on their meanings: restriction, expansion and static.

Politicization of discussions of Covid-19 on Twitter

2021 ◽  
Vol 1 (6) ◽  
pp. 3-11
Author(s):  
Irina G. Ovchinnikova ◽  
◽  
Liana M. Ermakova ◽  
Diana M. Nurbakova ◽  
◽  
...  
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Semantic Analysis ◽  
Medical Information ◽  
Information Cascades ◽  
Medical Problems ◽  
Significant Information ◽  
Information Distortion ◽  
Public Figures ◽  
English Speaking

Power of social media including Twitter for English speaking community to shape public opinion becomes critical during the current pandemic because of misinformation. The existing studies on spreading misinformation on social media hypothesise that the initial message is fake. In contrast, we focus on information distortion occurring in cascades as the initial message about the Covid-19 treatment is quoted or receives a reply. Public persons discuss medical information on Twitter providing fast and simple response to complex medical problems that users find very attractive to follow. Followers generate information cascades while quoting and commenting on the initial message. In the cascades, medical information from the initial tweet is often distorted. The discussion of the Covid-19 treatment in the cascades is politicized according to users’ political sympathies. We show a significant information shift in cascades initiated by public figures during the Covid-19 pandemic. The study provide valuable insights for the semantic analysis of information distortion.

A Sentiment Analysis and Role of Twitter for Health Communications

2022 ◽  
pp. 188-205
Author(s):  
Erkan Çiçek ◽  
Uğur Gündüz
Keyword(s):  
Social Media ◽  
Crisis Management ◽  
Semantic Analysis ◽  
Social Media Data ◽  
Global Pandemic ◽  
The Difference ◽  
Textual Content ◽  
The Impact ◽  
Media Data

Social media has been in our lives so much lately that it is an undeniable fact that global pandemics, which constitute an important part of our lives, are also affected by these networks and that they exist in these networks and share the users. The purpose of making this hashtag analysis is to reveal the difference in discourse and language while analyzing Twitter data and to evaluate the effects of a global pandemic crisis on language, message, and crisis management with social media data. This form of analysis is typically completed through amassing textual content data then investigating the “sentiment” conveyed. Within the scope of the study, 11,300 Twitter messages posted with the #stayhome hashtag between 30 May 2020 and 6 June 2020 were examined. The impact and reliability of social media in disaster management could be questioned by carrying out a content analysis based totally on the semantic analysis of the messages given on the Twitter posts with the phrases and frequencies used.

scholarly journals Earthquake Shake Detecting by Data Mining from Social Network Platforms

2020 ◽  
Vol 10 (3) ◽  
pp. 812
Author(s):  
Yu-Jung Chuo
Keyword(s):  
Social Media ◽  
Semantic Analysis ◽  
Bulletin Board ◽  
Tree Model ◽  
Detection Model ◽  
The Social ◽  
Network Platform ◽  
Scale Detection ◽  
Central Weather Bureau ◽  
Social Media Network

This study used social media posts of the related effect of earthquakes to derive seismic shake scale distributions in regions of Taiwan and compared it with the regional seismic scale reported by the Central Weather Bureau (CWB) of Taiwan. This study conducted a context searching to scrawl the relationship phrase on the social media network platform, PTT bulletin board system (BBS), to detect the earthquake shake scale using the keywords of the context. In this investigation a decision tree model for analyzing the semantic words from the context of the target event to detect the earthquake shake scale was devised. The results indicate that we can pick out the keywords to use to detect the earthquake shake scale at about 85%. Furthermore, the results of the derived shake scale show that the four studied cases are in a good agreement with the presented news from the CWB of Taiwan. In this study, the author attempted to develop a quick earthquake shake scale detection model by semantic analysis of the collected earthquake disaster information reported on the social media network platform.

scholarly journals Communicative Functions of Hashtags

2018 ◽  
Vol 15 (1) ◽  
pp. 56-62
Author(s):  
Aleksandra Laucuka
Keyword(s):  
Social Media ◽  
Literature Review ◽  
Initial Function ◽  
Semantic Analysis ◽  
Combined Approach ◽  
Communicative Functions ◽  
Brand Marketing ◽  
The Social ◽  
Restricted Volume

Abstract Despite the initial function of hashtags as tools for sorting and aggregating information according to topics, the social media currently witness a diversity of uses diverging from the initial purpose. The aim of this article is to investigate the communicative functions of hashtags through a combined approach of literature review, field study and case study. Different uses of hashtags were subjected to semantic analysis in order to disclose generalizable trends. As a result, ten communicative functions were identified: topic-marking, aggregation, socializing, excuse, irony, providing metadata, expressing attitudes, initiating movements, propaganda and brand marketing. These findings would help to better understand modern online discourse and to prove that hashtags are to be considered as a meaningful part of the message. A limitation of this study is its restricted volume.

scholarly journals SUMBANGAN BAHASA INDONESIA TERHADAP PEMEKARAN KOSAKATA BAHASA BUGIS: KASUS PENGGUNAAN BAHASA BUGIS PADA MEDIA SOSIAL FACEBOOK (The Contribution of Indonesian Language on Buginese Vocabulary Development: A Case Study of the Use of Buginese on Facebook Social Media)

SAWERIGADING ◽  
2019 ◽  
Vol 25 (2) ◽  
pp. 107
Author(s):  
Muhammad Darwis ◽  
Kamsinah Kamsinah
Keyword(s):  
Social Media ◽  
Research Strategies ◽  
Loan Words ◽  
Code Mixing ◽  
Word Categorization ◽  
The Social ◽  
Data Source ◽  
Media Data ◽  
Bahasa Indonesia

AbstrakThe aim of this research is: (1) to identify the forms and categories of Indonesian words that are absorbed into Buginese sentences and (2) to reveal the reasons for the use of Indonesian elements into Buginese sentences by Facebookers in the social media. Data on this qualitative research obtained from social media ‘Facebook’. The data source of this research is the Facebookers who are members of the MABBASA UUGIE KU PESBU’ group, November 2013 to April 2014 edition. Data analyzed are Buginese sentences consisting of three to five examples of Buginese sentences containing Indonesian elements in the form of words, phrases or clauses taken purposively. Furthermore, the analysis was carried out with grounded research strategies. The results of this research indicate that (1) Buginese language can survive as a means of communication within Buginese ethnic groups when writing on the social media ‘Facebook’, due to they have obtained vocabulary contributions from Indonesian in the form of the basic word, affixation word, and phrase. In word categorization, the loan words consist of nouns, verbs, adjectives, and conjunctions. Then, (2) the use of the Indonesian language elements has four main reasons, namely (a) filling in the blanks, (b) adding equivalence variations, (c) clarifying the meaning, and (d) interference. Reasons (a) to (c) can take the form of code-mixing and code-switching. AbstrakPenelitian ini bertujuan: (1) mengidentifikasi bentuk dan kategori kata bahasa Indonesia (bI) yang  terserap ke dalam kalimat-kalimat bahasa Bugis (bB) dan (2) mengungkap alasan-alasan penggunaan unsur-unsur bI tersebut ke dalam kalimat bB oleh para Facebooker di media sosial. Data penelitian kualitatif ini diperoleh dari media sosial Facebook. Sumber data penelitian ini ialah para Facebooker yang menjadi anggota grup MABBASA UUGIE KU PESBU’ edisi bulan November 2013 s.d. bulan April 2014. Data yang dianalisis ialah kalimat-kalimat ber-bB yang terdiri atas tiga sampai lima contoh kalimat ber-bB yang berisi unsur-unsur bI, yang berupa kata, frasa, atau klausa, yang diambil secara purposif. Selanjutnya, analisis dilakukan dengan upaya grounded research. Hasil penelitian ini menunjukkan bahwa (1) bB dapat bertahan hidup sebagai sarana perhubungan intern suku Bugis dalam komunikasi tulisan sosial Facebook karena memperoleh sumbangan kosakata bI yang berbentuk kata dasar, kata berimbuhan, dan frasa atau ungkapan. Dari segi kategorisasi kata, unsur-unsur serapan tersebut terdiri atas kata benda, kata kerja, kata sifat, dan kata sambung. Kemudian, (2) penggunaan unsur-unsur bI tersebut memiliki empat alasan utama, yaitu (a) mengisi kekosongan, (b) menambah variasi kesepadanan, (c) memperjelas pemaknaan, dan (d) interferensi. Alasan (a) sampai dengan (c) dapat mengambil bentuk campur kode dan alih kode.   

scholarly journals Suicide Prediction on Social Media by Implementing Sentiment Analysis along with Machine Learning

2019 ◽  
Vol 8 (2) ◽  
pp. 4833-4837
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Semantic Analysis ◽  
Machine Learning Techniques ◽  
Suicide Prediction ◽  
The People ◽  
Learning Techniques ◽  
Use Of Social Media ◽  
Day By Day

Technology is growing day by day and the influence of them on our day-to-day life is reaching new heights in the digitized world. Most of the people are prone to the use of social media and even minute details are getting posted every second. Some even go to the extent of posting even suicide related issues. This paper addresses the issue of suicide and is predicting the suicide issues on social media and their semantic analysis. With the help of Machine Learning techniques and semantic analysis of sentiments the prediction and classification of suicide is done. The model of approach is a four-tier approach, which is very beneficial as it uses the twitter4J data by using weka tool and implementing it on WordNet. The precision and accuracy aspects are verified as the parameters for the performance efficiency of the procedure. We also give a solution for the lack of resources regarding the terminological resources by providing a phase for the generation of records of vocabulary also.

scholarly journals FRIDZ: A Framework for Real-time Identification of Disaster Zones

2019 ◽  
Author(s):  
Abhisek Chowdhury
Keyword(s):  
Social Media ◽  
Real Time ◽  
Language Processing ◽  
Semantic Analysis ◽  
Flood Events ◽  
Catastrophic Event ◽  
Real Time Identification ◽  
Potential Disaster ◽  
Extract Information

Social media feeds are rapidly emerging as a novel avenue for the contribution and dissemination of geographic information. Among which Twitter, a popular micro-blogging service, has recently gained tremendous attention for its real-time nature. For instance, during floods, people usually tweet which enable detection of flood events by observing the twitter feeds promptly. In this paper, we propose a framework to investigate the real-time interplay between catastrophic event and peo-ples’ reaction such as flood and tweets to identify disaster zones. We have demonstrated our approach using the tweets following a flood in the state of Bihar in India during year 2017 as a case study. We construct a classifier for semantic analysis of the tweets in order to classify them into flood and non-flood categories. Subsequently, we apply natural language processing methods to extract information on flood affected areas and use elevation maps to identify potential disaster zones.

Sign in / Sign up

Export Citation Format

Share Document