Personality Prediction from Social Media Text: An Overview

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.

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

OJIN: The Online Journal of Issues in Nursing ◽

10.3912/ojin.vol17no03man03 ◽

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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Modelling personality prediction from user's posting on social media

10.1109/iccsai53272.2021.9609711 ◽

2021 ◽

Author(s):

Derwin Suhartono

Keyword(s):

Social Media ◽

Personality Prediction

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Exploring Trending Topics of Social Media Text with VoronoiTopicCloud Provide Useful and Intuitive Insights into Social Media Texts

10.1109/icnlp52887.2021.00006 ◽

2021 ◽

Author(s):

Yuhui Wang ◽

Feng Zhou ◽

Xiaoyong Li

Keyword(s):

Social Media ◽

Social Media Text ◽

Spam Detection on Social Media Using Semantic Convolutional Neural Network

International Journal of Knowledge Discovery in Bioinformatics ◽

10.4018/ijkdb.2018010102 ◽

2018 ◽

Vol 8 (1) ◽

pp. 12-26 ◽

Cited By ~ 16

Author(s):

Gauri Jain ◽

Manisha Sharma ◽

Basant Agarwal

Keyword(s):

Neural Network ◽

Social Media ◽

Convolutional Neural Network ◽

State Of The Art ◽

Spam Detection ◽

Learning Technology ◽

The Social ◽

Social Media Text ◽

Current Article ◽

Semantic Layer

This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, especially in case of text within the limited number of characters. Effective spam detection requires more number of efficient features to be learned. In the current article, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an added semantic layer on the top of it. The resultant model is known as a semantic convolutional neural network (SCNN). A semantic layer is composed of training the random word vectors with the help of Word2vec to get the semantically enriched word embedding. WordNet and ConceptNet are used to find the word similar to a given word, in case it is missing in the word2vec. The architecture is evaluated on two corpora: SMS Spam dataset (UCI repository) and Twitter dataset (Tweets scrapped from public live tweets). The authors' approach outperforms the-state-of-the-art results with 98.65% accuracy on SMS spam dataset and 94.40% accuracy on Twitter dataset.

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