Machine learning-based guilt detection in text

In this study, we present guilt detection, a novel task in natural language processing aimed at detecting guilt in text. For this task we developed a dataset and a set of base classifiers. Guilt is a complex emotion that arises when individuals reflect on past mistakes or failures to uphold their own moral standards.¹. It is often experienced when people feel responsible for wronging or harming others, whether real or imagined.². Any interpersonal deficiencies that are identified are accompanied by a desire to correct them³.

In text processing, guilt can be detected through linguistic signs that indicate a sense of responsibility or remorse for past actions or events. These markers may include words or phrases that indicate a sense of responsibility or guilt, such as “I’m sorry” or “I wish I’d done things differently” or “I should have known better” or “I.” Take responsibility for what happened. It is important to note that guilt can manifest itself more subtly in text through indirect or vague language, self-deprecation, or avoidance of specific subjects or individuals.^4,5. As such, a comprehensive understanding of guilt in text requires a nuanced approach that considers both explicit and implicit linguistic markers.

Furthermore, the experience of guilt may be influenced by cultural and social norms and individual differences in personality, cognitive processing, and emotional regulation.⁶. Thus, any analysis of guilt in texts needs to consider these factors and their potential influence on the expression and interpretation of guilt-related language. Detecting and analyzing guilt in text is a challenging but essential task for natural language processing research, with potential applications in mental health, social media analysis, and criminal justice.

Sharing emotional experiences is a critical component of the emotional process (the mental and physical events that occur when a person experiences an emotion. It includes the cognitive interpretation and subjective experience of an emotion, and the resulting behavioral and physical responses. .⁷), as observed⁸; Also, in⁹The authors found that sharing emotional experiences on the Internet has become a critical part of everyday life On Contemporary cultures. This can explain the popularity of emotion-related research in many scientific disciplines, including computer science, especially natural language processing (NLP), written text to determine the presence, assess their intensity and polarity, and compare different emotional experiences. Despite considerable interest in detecting emotions, there is still a considerable research void regarding the in-depth examination of specific emotions such as guilt.

Guilt comes in many forms such as anticipatory, existential and reactive guilt²It is often experienced when people feel responsible for wrongdoing or fail to uphold their own moral standards.¹; As a result, it often indicates an understanding of the experiences of others and a desire to correct any personal mistakes.³. Like other self-conscious emotions, guilt can have a negative impact on one’s mental health if overdone.¹⁰It has attracted the interest of researchers in various disciplines, from psychology to neuroscience and computer science, and in the latter, especially as a sub-field in natural language processing, guilt has been introduced as a class in many NLP emotion detection subtasks. Research activities.

Guilt is a complex emotion that is crucial to our daily lives. Although it has been extensively studied in psychology and philosophy, natural language processing (NLP) has not yet received the same attention. In social media analysis, detecting guilt in user-generated content can help social media platforms develop more targeted and effective interventions for users experiencing negative emotions. In legal contexts, guilt detection can be used to assess the veracity of legal statements and identify potential suspects.

As noted in , the mental health consequences of excessive guilt are taken into account¹⁰ Several research studies support the association of guilt with suicidality in clinical populations^3,11,12There is obvious importance and value in learning ways to recognize when, when, and how a person experiences guilt.

Despite the potential applications, to our knowledge, no extensive research in NLP has focused on guilt detection as a primary topic of study. Previous studies such as^13,14 A multi-class emotion detection task included only guilt. Our paper aims to fill this research gap by constructing a binary crime detection dataset and evaluating the performance of traditional and deep learning models on this dataset.

One could argue that guilt is included as one of the classes in existing multiclass emotion detection datasets. However, those studies focused on detecting multiple emotions, not specifically guilt. By creating a binary guilt detection dataset and developing models specifically for guilt detection, this study provides a more focused approach to understanding and detecting this particular emotion. Additionally, existing datasets may lack sufficient examples of offending events or may have noise and bias from other emotions included in the dataset. Creating a dedicated crime detection dataset helps address these issues and provides a more accurate and reliable way to detect crime.

In summary, the novelty of our paper lies in focusing on guilt detection as a primary study topic in NLP and developing a binary guilt detection dataset. We believe that this research can lead to a better understanding of guilt as an emotion and its applications in various industries.

The main contributions of this paper are:

• A study of guilt detection from text using NLP techniques

• Development of a multi-source dataset for binary crime detection,

• Development of basic models for specific dataset,

• In-depth analysis of dataset and models

The rest of the paper consists of the following sections: “Literature Review”, which shows works on emotion detection, particularly those focusing on guilt; “Dataset Development” describes the techniques used to acquire and construct the dataset; “Benchmark Experiments” featuring setups for basic model experiments; and “Results and Discussion”, where a preliminary analysis of the main results is provided. Finally, the paper ends with “Conclusion and Future Work” suggesting future directions for research on guilt.