scholarly journals A taxonomy of Malay social media text

2019 ◽  
Vol 16 (1) ◽  
pp. 465
Author(s):  
Ruhaila Maskat ◽  
Yuda Munarko
Keyword(s):  
Social Media ◽  
Text Analytics ◽  
Commercial Products ◽  
Sentence Construction ◽  
Social Media Text ◽  
Formal Taxonomy

<span>In this paper, we proposed a preliminary taxonomy of Malay social media text. Performing text analytics on Malay social media text is a challenge. The formal Malay language follows specific spelling and sentence construction rules. However, the Malay language used in social media differs in both aspects. This impedes the accuracy of text analytics. Due to the complexity of Malay social media text, many researches has chosen to focus on classifying the formal Malay language. To the best of our knowledge, we are the first to propose a formal taxonomy for Malay text in social media. Narrow and informal</span><span lang="EN-GB"> categorisations</span><span> of Malay social media text can be found amidst efforts to pre-process social media text, yet cherry-picked only some categories to be handled. We have differentiated Malay social media text from the formal Malay language by identifying them as Social Media Malay Language or SMML. They consists of </span><em><span lang="EN-GB">spelling variations</span></em><span lang="EN-GB">, <em>Malay-English mix sentence</em>, <em>Malay-spelling English words</em>, <em>slang-based words,</em> <em>vowel-les words, number suffixes </em>and<em> manner of expression.</em></span><span>This taxonomy is expected to serve as a guideline in research and commercial products.</span>

Deep Learning for Social Media Text Analytics

2021 ◽  
pp. 68-91
Author(s):  
Anto Arockia Rosaline R. ◽  
Parvathi R.
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Text Analytics ◽  
Learning Techniques ◽  
The Social ◽  
Social Media Text ◽  
High Quality Information ◽  
Traditional Natural ◽  
Processing Techniques

Text analytics is the process of extracting high quality information from the text. A set of statistical, linguistic, and machine learning techniques are used to represent the information content from various textual sources such as data analysis, research, or investigation. Text is the common way of communication in social media. The understanding of text includes a variety of tasks including text classification, slang, and other languages. Traditional Natural Language Processing (NLP) techniques require extensive pre-processing techniques to handle the text. When a word “Amazon” occurs in the social media text, there should be a meaningful approach to find out whether it is referring to forest or Kindle. Most of the time, the NLP techniques fail in handling the slang and spellings correctly. Messages in Twitter are so short such that it is difficult to build semantic connections between them. Some messages such as “Gud nite” actually do not contain any real words but are still used for communication.

Modeling Factuality Judgments in Social Media Text

2014 ◽  
Author(s):  
Sandeep Soni ◽  
Tanushree Mitra ◽  
Eric Gilbert ◽  
Jacob Eisenstein
Keyword(s):  
Social Media ◽  
Social Media Text

Improving sentiment scoring mechanism: a case study on airline services

2018 ◽  
Vol 118 (8) ◽  
pp. 1578-1596 ◽  
Author(s):  
Wandeep Kaur ◽  
Vimala Balakrishnan
Keyword(s):  
Social Media ◽  
Content Type ◽  
Exclamation Mark ◽  
Semantic Orientation ◽  
Social Media Text ◽  
Major Airlines ◽  
Using Data ◽  
Sentiment Score ◽  
Repeated Letter ◽  
Facebook Pages

Purpose The purpose of this paper is to investigate the effect of including letter repetition commonly found within social media text and its impact in determining the sentiment scores for two major airlines in Malaysia. Design/methodology/approach A Sentiment Intensity Calculator (SentI-Cal) was developed by assigning individual weights to each letter repetition, and tested it using data collected from official Facebook pages of the airlines. Findings Evaluation metrics indicate that SentI-Cal outperforms the baseline tool Semantic Orientation Calculator (SO-CAL), with an accuracy of 90.7 percent compared to 58.33 percent for SO-CAL. Practical implications A more accurate sentiment score allows airline services to easily obtain a better understanding of the sentiments of their customers, hence providing opportunities in improving their airline services. Originality/value Proposed mechanism calculates sentiment intensity of social media text by assigning individual weightage to each repeated letter and exclamation mark thus producing a more accurate sentiment score.

Communication Technology and Social Media: Opportunities and Implications for Healthcare Systems

2012 ◽  
Vol 17 (3) ◽  
Author(s):  
Betsy Weaver ◽  
Bill Lindsay ◽  
Betsy Gitelman
Keyword(s):  
Social Media ◽  
Communication Technology ◽  
Technology Implementation ◽  
Text Messaging ◽  
Current Evidence ◽  
Positive Outcomes ◽  
Social Media Text ◽  
E Mail ◽  
Improve Patient

Electronic patient education and communications, such as email, text messaging, and social media, are on the rise in healthcare today. This article explores potential uses of technology to seek solutions in healthcare for such challenges as modifying behaviors related to chronic conditions, improving efficiency, and decreasing costs. A brief discussion highlights the role of technologies in healthcare informatics and considers two theoretical bases for technology implementation. Discussion focuses more extensively on the ability and advantages of electronic communication technology, such as e-mail, social media, text messaging, and electronic health records, to enhance patient-provider e-communications in nursing today. Effectiveness of e-communication in healthcare is explored, including recent and emerging applications designed to improve patient–provider connections and review of current evidence supporting positive outcomes. The conclusion addresses the vision of nurses’ place in the vanguard of these developments.

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