# Utilizing Machine Learning for Detecting Harmful Situations by Audio and Text
## 2 Related Work
### Technologies behind Emotion Recognition
#### 2.2.1 Text Emotion Recognition
The dataset comprised text from Chinese microblogs. They used an emotion model to extract the cause components of fine-grained emotions. [PERSON] et al. [35] addressed the issue of emotion cause extraction, extracting stimuli, or the cause of an emotion. They proposed an event-driven emotion cause extraction method, in which a seven-tuple representation of events was used. Based on this structured representation of events and inclusion of lexical features, they designed a convolutional kernel-based learning method to identify emotion-causing events using syntactic structures. [PERSON] and [PERSON] [36; 37] developed Safebot, a chatbot system that converses with humans. Safebots use human feedback to identify offensive behaviors. When Safebot was told that it said something offensive, it apologized and added the offensive sentence to its database. It then avoided using those sentences. While our proposed artificial assisting agent generally relies on its ability to perform sentiment analysis, it also relies on its ability of detecting hate speech, bullying, and insulting speech from two perspectives. From the perspective of children, we would like to detect bullying directed at children with special needs to protect them. From the viewpoint of those who interact with these children, we would like to observe that the child behaves appropriately and refrains from speaking in an insulting manner. The insulting sentences in our domain can be a result of innocent intentions, and in most cases, they do not contain language that is considered as a bullying behavior. Some prior work addressed the aforementioned issue. [PERSON] et al. [38] used [PERSON]'s regression model and natural language processing (NLP) features to detect hate speech in online user comments from two domains. This approach outperformed a state-of the-art DL approach. [PERSON] et al. [39] detected abusive Hebrew texts in comments on Facebook by using a highly sparse n-gram representation of letters. Since comments on social media are usually short, they suggested four dimension reduction methods that classify similar words into groups and showed that the character n-gram representations outperformed all the other representations. [PERSON] et al. [40] proposed integrating expert knowledge into a system for cyberbullying detection. Using a multicriteria evaluation system, they obtained a better understanding of bullying behavior of YouTube users and their characteristics through expert knowledge. Based on this knowledge, the system assigned a score to users that represented their level of bullying based on the history of their activities. In a related study, [PERSON] et al. [41] focused on race talk and hate speech. They described technologies, theories, and experiences that enabled the conversational agent to handle race talk and examine the generative connections between race, technology, conversation, and CAs. By drawing together the technological-social interactions involved in race talk and hate speech, they pointed out the need for developing generative solutions focusing on this issue. In our study, we utilized Heb-BERT [5], a Hebrew version of BERT, to analyze and classify texts. The BERT model's ability to capture context and meaning is useful for understanding and analyzing natural language data. Additionally, its fine-tuning capabilities allow for high accuracy in analyzing specific datasets. By utilizing BERT for research, we gain a more nuanced and accurate understanding of natural language data, making it possible to identify patterns and trends that are challenging to detect using other methods. To evaluate the performance of Heb-BERT in our study, we compared the accuracy of various machine learning and deep learning methods on TF-IDF sentences, as well as on the Heb-BERT version of the training set, and we also fine-tuned the model using our training data.