Sentiment Analysis Using Machine Learning Algorithms and Text Mining to Detect Symptoms of Mental Difficulties Over Social Media

Bouarara, Hadj Ahmed

doi:10.4018/ijissc.2021040101

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…In (Bouarara, 2022), the authors considered the main goal of their research to identify the behavior of people suffering from mental problems among Twitter users in order to support them. For this purpose, they have used text mining strategy relying on machine learning techniques such as naïve Bayes and knearest neighbours.…”

Section: Previous Workmentioning

confidence: 99%

DDdeep: deep learning-based text analysis for depression illness detection on social media posts

Keyvanpour

Mehrmolaei

Gholami

2022

Preprint

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Recently, depression has been raised as one of the most popular mental health disorders in the world. Also, social networks can be considered a valuable resource for mental health research due to the tendency of users for sharing their thoughts and feelings. On the other hand, text analysis of user posts relying on neural networks for such research is increasing. Neural networks have recently achieved significant success in text analysis because of the ability to automatically extract distinguishing features from data. However, neural networks are ignored the temporal and sequential nature of users' posts on social networks which affects the accuracy of the results. This shortcoming prompted us to present a more efficient method considering the sequential and temporal nature of social media users' posts. Thus, we have proposed a deep learning-based hybrid method called DDdeep to handle the mentioned challenge. There are three main features in our method, which are (1) text analysis relying on the temporal and sequential nature of posts, (2) identifying depressed users only by considering how users use language, and (3) remembering decisions because of the dependence of each post on previous posts. The DDdeep method has integrated a convolutional neural network (CNN) to extract more important features and long-short term memory (LSTM) to remember previous decisions. Our method identifies the depressed users by 78% precision, 70% recall, and 73% F1-score. Therefore, the evaluation results of our method are acceptable and competitive compared to other valid methods in this field.

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Section: Previous Workmentioning

confidence: 99%

DDdeep: deep learning-based text analysis for depression illness detection on social media posts

Keyvanpour

Mehrmolaei

Gholami

2022

Preprint

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“…e theory of machine learning is constantly evolving, which has led to an increase in the number of academics who are applying machine learning algorithms to the study of sentiment recognition of university online public opinion. ese scholars have found that their efforts have been fruitful [4,5]. An opinion evolution system model based on complex agent networks was constructed by the literature [6] in order to analyze the influence of various factors within the model on the development of public opinion.…”

Section: Introductionmentioning

confidence: 99%

A Novel Affective Analysis System Modeling Method Integrating Affective Cognitive Model and Bi-LSTM Neural Network

Yang

Zhou

Jing

2022

Computational Intelligence and Neuroscience

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The severity of mental health issues among college students has increased over the past few years, having a significant negative impact on not only their academic performance but also on their families and even society as a whole. Therefore, one of the pressing issues facing college administrators right now is finding a method that is both scientific and useful for determining the mental health of college students. In pace with the advancement of Internet technology, the Internet has become an important communication channel for contemporary college students. As one of the main forces in the huge Internet population, college students are at the stage of growing knowledge and being most enthusiastic about new things, and they like to express their opinions and views on study life and social issues and are brave to express their emotions. These subjective text data often contain some affective tendencies and psychological characteristics of college students, and it is beneficial to dig out their affective tendencies to further understand what they think and expect and to grasp their mental health as early as possible. In order to address the issue of assessing the mental health of college students, this study makes an effort to use public opinion data from the university network and suggests a college student sentiment analysis model based on the OCC affective cognitive model and Bi-LSTM neural network. In order to label three different types of positive, negative, and neutral sentiment on the microblog text of college network public opinion, we first design a sentiment rule system based on the OCC affective cognition elicitation mechanism. In order to effectively and automatically identify the sentiment state of college students in the network public opinion, this study uses a Bi-LSTM neural network to classify the preprocessed college network public opinion data. Finally, this study performs comparison experiments to confirm the validity of the Bi-LSTM neural network sentiment recognition algorithm and the accuracy of the OCC sentiment rule labeling system. The findings show that the college student sentiment recognition effect of the model is significantly enhanced when the OCC sentiment rule system is used to label the college network public opinion data set as opposed to the naturally labeled data set. In contrast to SVM and other classification models like CNN and LSTM, the Bi-LSTM neural network-based classification model achieves more satisfactory classification results in the recognition of college opinion sentiment.

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“…Although these methods perform well in terms of classification accuracy, they still face many difficulties. The emotion dictionary-based approach, which takes the emotion dictionary as the main basis for judging and commenting on emotion polarity [3], relies on a large amount of manual intervention, such as dictionary construction and judgment rule formulation, etc. It is difficult to cope with the emergence of new and unknown words and has the domain dependence problem [4].…”

Section: Introductionmentioning

confidence: 99%

Hybrid neural network model based on multi-head attention for English text emotion analysis

Li¹

2018

ICST Transactions on Scalable Information Systems

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Sentiment Analysis Using Machine Learning Algorithms and Text Mining to Detect Symptoms of Mental Difficulties Over Social Media

Cited by 4 publications

References 6 publications

DDdeep: deep learning-based text analysis for depression illness detection on social media posts

DDdeep: deep learning-based text analysis for depression illness detection on social media posts

A Novel Affective Analysis System Modeling Method Integrating Affective Cognitive Model and Bi-LSTM Neural Network

Hybrid neural network model based on multi-head attention for English text emotion analysis

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