RoboTech: 2017

Undergraduates' Interpretation on WhatsApp Smiley Emoji

ABSTRACT:

Undergraduate students, who are digital native, are keen on using emoji (smileys and ideograms) frequently to express themselves emotionally in their digital communication such as WhatsApp Messenger.

Nevertheless, sometimes, they got into misunderstanding due to the different emoji's interpretation between the sender and the recipient.

Research investigating emoji is still relatively new and this study discusses the diverse interpretations of WhatsApp emoji specifically the smileys among Malaysian undergraduates in a public university.

This study attempted to investigate 210 undergraduates' interpretations of 75 smiley (face-like) meanings in WhatsApp Messenger.

The respondents were asked to give feedback in self-administrated survey questionnaire to gather information on their interpretation of the smileys used in WhatsApp.

A descriptive analysis was conducted on the students' interpretations and the findings disclosed that although the students interpreted a few smileys correctly, they did not know the intended meaning of most of the smileys correctly.

The results of this study suggested that the students should know the meaning of the smiley/ emoji used in their digital conversation to able to understand its intended use and to avoid miscommunication in their digital communication.

For WhatsApp users, the findings will be beneficial to emphasize the need to understand the emoji's intended meaning for future tolerant and wiser use.

KEYWORD:

WhatsApp, emoji, smiley, interpretation, undergraduate.

PUBREF:

https://ejournals.ukm.my/mjc/article/view/22621/7134

SEMREF:

https://www.semanticscholar.org/paper/Undergraduates'-Interpretation-on-WhatsApp-Smiley-Annamalai-Salam/ab53467155181400fb56e64d8c8a19800dad6e2e

RGTREF:

https://www.researchgate.net/publication/321969640_Undergraduates%27_Interpretation_on_WhatsApp_Smiley_Emoji

DOCREF:

http://repo.uum.edu.my/24505/1/JK%2033%204%202017%2089%20103.pdf

GIDREF:

https://app.razzi.my/findgref?gid=1XORRFSqiCVSsWHf6YxvqJ8QTT7y1oHXj

Do Intelligent Robots Need Emotion?

Luiz Pessoa 1

Affiliations expand

PMID: 28735707 PMCID: PMC6237080 DOI: 10.1016/j.tics.2017.06.010

Free PMC article

Abstract

What is the place of emotion in intelligent robots? Researchers have advocated the inclusion of some emotion-related components in the information-processing architecture of autonomous agents. It is argued here that emotion needs to be merged with all aspects of the architecture: cognitive-emotional integration should be a key design principle.

https://www.sciencedirect.com/science/article/abs/pii/S1364661317301341

How To Build Word Embeddings

Word embeddings are a type of word representation that allows words with similar meaning to have a similar representation.

They are a distributed representation for text that is perhaps one of the key breakthroughs for the impressive performance of deep learning methods on challenging natural language processing problems.

In this post, you will discover the word embedding approach for representing text data.

After completing this post, you will know:

(1) What the word embedding approach for representing text is and how it differs from other feature extraction methods.

(2) That there are 3 main algorithms for learning a word embedding from text data.

(3) That you can either train a new embedding or use a pre-trained embedding on your natural language processing task.

https://machinelearningmastery.com/what-are-word-embeddings/

How to Use Word Embedding Layers for Deep Learning with Keras

https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/

How to Develop Word Embeddings in Python with Gensim

Word embeddings are a modern approach for representing text in natural language processing.

Word embedding algorithms like word2vec and GloVe are key to the state-of-the-art results achieved by neural network models on natural language processing problems like machine translation.

In this tutorial, you will discover how to train and load word embedding models for natural language processing applications in Python using Gensim.

After completing this tutorial, you will know:

(1) How to train your own word2vec word embedding model on text data.

(2) How to visualize a trained word embedding model using Principal Component Analysis.

(3) How to load pre-trained word2vec and GloVe word embedding models from Google and Stanford.

https://machinelearningmastery.com/develop-word-embeddings-python-gensim/