Text Classification based Behavioural Analysis of WhatsApp Chats

WhatsApp is used by millions of users to express emotions and share feelings. The model is presented in this paper aims to perform sentimental and emotional analysis using textual messages and emojis used in WhatsApp chats. Code switching, which is quite prevalent over online conversations, is handled by the model by unifying and converting all the texts to a standard form. For each subject, multiple chats are taken; translated and using a neural network, each sentence and emoji is scored in a dimensional form. The composition of the emotions expressed by the subject (out of Happy, Sad, Bored, Fear, Anger and Excitement) are defined. The scores are added up for each subject. Throughout the analysis, the behavioral traits are extracted. It is determined that, if the subject likes to use emojis and if they use it as a replacement for words or as an add-on to express their emotions better. It is also observed that if the subject behaves differently on text according to the person in front of them with regard to these emotions and finally, if the subject is an introvert or extrovert.

[1]  Yan Ge,et al.  Frontal EEG Asymmetry and Middle Line Power Difference in Discrete Emotions , 2018, Front. Behav. Neurosci..

[2]  Ning Wang,et al.  Learning from the ubiquitous language: an empirical analysis of emoji usage of smartphone users , 2016, UbiComp.

[3]  Sunil Joshi,et al.  Sentiment Analysis on WhatsApp Group Chat Using R , 2019, Data, Engineering and Applications.

[4]  M. S. S. Ali,et al.  Trends and Impact of WhatsApp as a Mode of Communication among Abu Dhabi Students , 2018 .

[5]  J. Russell.  A circumplex model of affect. , 1980 .

[6]  S. Vazire.  PERSONALITY PROCESSES AND INDIVIDUAL DIFFERENCES Who Knows What About a Person ? The Self – Other Knowledge Asymmetry ( SOKA ) Model , 2010 .

[7]  Ke Xu,et al.  Extroverts tweet differently from introverts in Weibo , 2018, EPJ Data Science.

[8]  Giovanni Acampora,et al.  A cognitive multi-agent system for emotion-aware ambient intelligence , 2011, 2011 IEEE Symposium on Intelligent Agent (IA).

[9]  Joachim Wagner,et al.  Code Mixing: A Challenge for Language Identification in the Language of Social Media , 2014, CodeSwitch@EMNLP.

[10]  K. Jisha,et al.  Whatsapp: A Trend Setter in Mobile Communication among Chennai Youth , 2014 .

[11]  Mohd Ridzwan Yaakub,et al.  A Review on Sentiment Analysis Techniques and Applications , 2019, IOP Conference Series: Materials Science and Engineering.

[12]  Impact of WhatsApp on youth: A Sociological Study , 2016 .

[13]  Ye Tian,et al.  Facebook sentiment: Reactions and Emojis , 2017, SocialNLP@EACL.

[14]  Aravind K. Joshi,et al.  Processing of Sentences With Intra-Sentential Code-Switching , 1982, COLING.

[15]  Maurizio Naldi,et al.  A review of sentiment computation methods with R packages , 2019, ArXiv.

[16]  Jan Holub,et al.  Emotion models for textual emotion classification , 2016 .

[17]  J. Russell,et al.  The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology , 2005, Development and Psychopathology.

[18]  Naveen Kumar,et al.  Survey Analysis on the usage and Impact of Whatsapp Messenger , 2017 .