A Unified System for Aggression Identification in English Code-Mixed and Uni-Lingual Texts

Wide usage of social media platforms has increased the risk of aggression, which results in mental stress and affects the lives of people negatively like psychological agony, fighting behavior, and disrespect to others. Majority of such conversations contains code-mixed languages[28]. Additionally, the way used to express thought or communication style also changes from one social media plat-form to another platform (e.g., communication styles are different in twitter and Facebook). These all have increased the complexity of the problem. To solve these problems, we have introduced a unified and robust multi-modal deep learning architecture which works for English code-mixed dataset and uni-lingual English dataset both.The devised system, uses psycho-linguistic features and very ba-sic linguistic features. Our multi-modal deep learning architecture contains, Deep Pyramid CNN, Pooled BiLSTM, and Disconnected RNN(with Glove and FastText embedding, both). Finally, the system takes the decision based on model averaging. We evaluated our system on English Code-Mixed TRAC 2018 dataset and uni-lingual English dataset obtained from Kaggle. Experimental results show that our proposed system outperforms all the previous approaches on English code-mixed dataset and uni-lingual English dataset.Comment: 10 pages, 5 Figures, 6 Tables, accepted at CoDS-COMAD 202

Machine Learning Models for Detecting Psychological Illnesses and Disorders of Text Authors

Pravodobno prepoznavanje bolesti gotovo je jednako važno kao i povećanje učinkovitosti njihovog liječenja. Rano se prepoznavanje uglavnom svede na odlazak kod liječnika potaknut pojavom prvih simptoma. Kod psihičkih bolesti i poremećaja situacija je značajno drukčija - ne samo zato što osim promjena u ponašanju pojedinca drugih simptoma gotovo da i nema, već zato što postoje istraživanja koja upućuju na to da se u govoru i pismu osoba koje pate od psihičkih poremećaja i bolesti mogu uočiti određeni uzorci i pravilnosti. Primjena računala ili, preciznije, strojnog učenja u prepoznavanju psihičkih problema autora teksta mogla bi biti iznimno korisna. Tada je očito nužno prikupiti dovoljnu količinu podataka, što je najjednostavnije postići koristeći brojne objave korisnika društvenih mreža. Autori skupa podataka SHMD napravili su upravo to - prikupili su objave korisnika \textit{Reddita} i označili ih poremećajima, odnosno bolestima, od kojih ti isti korisnici pate (ili su, u slučaju da su zdravi, označeni kao dio kontrolne skupine). Koristeći navedeni skup podataka kroz ovaj je rad istražena primjenjivost različitih modela temeljenih na LSTM-u treniranih na različite načine. Prvi način se pokazao užasno sporim i nedovoljno kvalitetnim, zbog čega nije bilo dovoljno vremena za detaljnije proučavanje drugih pristupa koji su davali obećavajuće rezultate.Being able to detect diseases early enough to facilitate their treatment is a task almost as important as increasing the efficiency of the treatment itself. But early detection mostly relies on appearance of symptoms that might prompt the person to talk to a doctor. When it comes to psychological disorders and illnesses, things are different - not only because they do not exhibit symptoms other than changes in behaviour, but because research has shown that certain patterns in speech and text produced by people suffering from certain disorders can be captured. Using computers, more specifically, machine learning to detect mental health issues of text authors might be quite beneficial. To do that, enough data has to be collected, so it is only natural to resort to social networks where users choose to share different content on daily basis. The authors of SHMD dataset aimed to do exactly that - gather user posts from Reddit and label them with the disorder the user suffers from (or assign the user the control group label if healthy). In this work using that dataset several LSTM-based models in different setups were trained, the first being rather time consuming without achieving satisfactory results, leaving less time to explore the more promising approach in the second setup

Machine Learning Models for Detecting Psychological Illnesses and Disorders of Text Authors

Pravodobno prepoznavanje bolesti gotovo je jednako važno kao i povećanje učinkovitosti njihovog liječenja. Rano se prepoznavanje uglavnom svede na odlazak kod liječnika potaknut pojavom prvih simptoma. Kod psihičkih bolesti i poremećaja situacija je značajno drukčija - ne samo zato što osim promjena u ponašanju pojedinca drugih simptoma gotovo da i nema, već zato što postoje istraživanja koja upućuju na to da se u govoru i pismu osoba koje pate od psihičkih poremećaja i bolesti mogu uočiti određeni uzorci i pravilnosti. Primjena računala ili, preciznije, strojnog učenja u prepoznavanju psihičkih problema autora teksta mogla bi biti iznimno korisna. Tada je očito nužno prikupiti dovoljnu količinu podataka, što je najjednostavnije postići koristeći brojne objave korisnika društvenih mreža. Autori skupa podataka SHMD napravili su upravo to - prikupili su objave korisnika \textit{Reddita} i označili ih poremećajima, odnosno bolestima, od kojih ti isti korisnici pate (ili su, u slučaju da su zdravi, označeni kao dio kontrolne skupine). Koristeći navedeni skup podataka kroz ovaj je rad istražena primjenjivost različitih modela temeljenih na LSTM-u treniranih na različite načine. Prvi način se pokazao užasno sporim i nedovoljno kvalitetnim, zbog čega nije bilo dovoljno vremena za detaljnije proučavanje drugih pristupa koji su davali obećavajuće rezultate.Being able to detect diseases early enough to facilitate their treatment is a task almost as important as increasing the efficiency of the treatment itself. But early detection mostly relies on appearance of symptoms that might prompt the person to talk to a doctor. When it comes to psychological disorders and illnesses, things are different - not only because they do not exhibit symptoms other than changes in behaviour, but because research has shown that certain patterns in speech and text produced by people suffering from certain disorders can be captured. Using computers, more specifically, machine learning to detect mental health issues of text authors might be quite beneficial. To do that, enough data has to be collected, so it is only natural to resort to social networks where users choose to share different content on daily basis. The authors of SHMD dataset aimed to do exactly that - gather user posts from Reddit and label them with the disorder the user suffers from (or assign the user the control group label if healthy). In this work using that dataset several LSTM-based models in different setups were trained, the first being rather time consuming without achieving satisfactory results, leaving less time to explore the more promising approach in the second setup

Machine Learning Models for Detecting Psychological Illnesses and Disorders of Text Authors

Pravodobno prepoznavanje bolesti gotovo je jednako važno kao i povećanje učinkovitosti njihovog liječenja. Rano se prepoznavanje uglavnom svede na odlazak kod liječnika potaknut pojavom prvih simptoma. Kod psihičkih bolesti i poremećaja situacija je značajno drukčija - ne samo zato što osim promjena u ponašanju pojedinca drugih simptoma gotovo da i nema, već zato što postoje istraživanja koja upućuju na to da se u govoru i pismu osoba koje pate od psihičkih poremećaja i bolesti mogu uočiti određeni uzorci i pravilnosti. Primjena računala ili, preciznije, strojnog učenja u prepoznavanju psihičkih problema autora teksta mogla bi biti iznimno korisna. Tada je očito nužno prikupiti dovoljnu količinu podataka, što je najjednostavnije postići koristeći brojne objave korisnika društvenih mreža. Autori skupa podataka SHMD napravili su upravo to - prikupili su objave korisnika \textit{Reddita} i označili ih poremećajima, odnosno bolestima, od kojih ti isti korisnici pate (ili su, u slučaju da su zdravi, označeni kao dio kontrolne skupine). Koristeći navedeni skup podataka kroz ovaj je rad istražena primjenjivost različitih modela temeljenih na LSTM-u treniranih na različite načine. Prvi način se pokazao užasno sporim i nedovoljno kvalitetnim, zbog čega nije bilo dovoljno vremena za detaljnije proučavanje drugih pristupa koji su davali obećavajuće rezultate.Being able to detect diseases early enough to facilitate their treatment is a task almost as important as increasing the efficiency of the treatment itself. But early detection mostly relies on appearance of symptoms that might prompt the person to talk to a doctor. When it comes to psychological disorders and illnesses, things are different - not only because they do not exhibit symptoms other than changes in behaviour, but because research has shown that certain patterns in speech and text produced by people suffering from certain disorders can be captured. Using computers, more specifically, machine learning to detect mental health issues of text authors might be quite beneficial. To do that, enough data has to be collected, so it is only natural to resort to social networks where users choose to share different content on daily basis. The authors of SHMD dataset aimed to do exactly that - gather user posts from Reddit and label them with the disorder the user suffers from (or assign the user the control group label if healthy). In this work using that dataset several LSTM-based models in different setups were trained, the first being rather time consuming without achieving satisfactory results, leaving less time to explore the more promising approach in the second setup