Nonetheless, an attitudinal revolution under the guise of inclusive masculinity theory (Anderson, 2009) maintains more liberal masculine identities are emerging. The pressure to perform within such boundaries has impacted upon gendered and sexual identities. In doing so, dance has been distanced from orthodox masculinity, which is framed in heterosexuality, homophobia, and anti-femininity identities. Male involvement in dance has been compared to effeminacy and homosexuality (Owen and Riley, 2020b), which has marginalised male participation. This thesis examines how child-centred research illuminates complex and intertwined social dynamics for boys in dance. Finally, the challenges of sentiment analysis are examined in order to define future directions. Then, it evaluates, compares, and investigates the approaches used to gain a comprehensive understanding of their advantages and disadvantages. This article discusses a complete overview of the method for completing this task as well as the applications of sentiment analysis. Sentiment analysis identifies and extracts subjective information from the text using natural language processing and text mining. These challenges create impediments to accurately interpreting sentiments and determining the appropriate sentiment polarity. However, the sentiment analysis and evaluation procedure face numerous challenges. People’s opinions can be beneficial to corporations, governments, and individuals for collecting information and making decisions based on opinion. Sentiment analysis is the process of gathering and analyzing people’s opinions, thoughts, and impressions regarding various topics, products, subjects, and services. The rapid growth of Internet-based applications, such as social media platforms and blogs, has resulted in comments and reviews concerning day-to-day activities. For the tasks of sentiment analysis and offensive language identification, the best performing model scored a weighted F1-Score of (66.8%, 90.5%), (59%, 70%) and (62.1%,75.3%) for Kannada, Malayalam and Tamil on sentiment analysis and offensive language identification respectively. Best scores on Tamil was achieved by DistilBERT subjected to cross entropy loss with soft parameter sharing as the architecture type. Maximum scores on Kannada and Malayalam were achieved by mBERT subjected to cross entropy loss and with an approach of hard parameter sharing. We apply two multi-task learning approaches to three Dravidian languages, Kannada, Malayalam, and Tamil. Analysis of fine-tuned models indicates the preference of multi-task learning over single task learning resulting in a higher weighted F1 score on all three languages. Experiments show that our multi-task learning model can achieve high results compared to single-task learning while reducing the time and space constraints required to train the models on individual tasks. Our framework is applicable to other sequence classification problems irrespective to the size of the datasets. This paper works with code-mixed YouTube comments for Tamil, Malayalam, and Kannada languages. The selection of these tasks is motivated by the lack of large labelled data for user-generated code-mixed datasets. Sentiment analysis and offensive language identification share similar discourse properties. It is challenging to obtain extensive annotated data for under-resourced languages, so we investigate whether it is beneficial to train models using multi-task learning. Some strengths and limitations of the study are discussed as well.
Despite contextual relevance, we presume that in socially and morally unacceptable events like rape and war, the valences of reactions alter to some extent: angry and sad usually become positive, while love, wow, and haha become negative. Although many users tend to mock and laugh at rape incidents and the victims, trend lines suggest that such expressions may not be consistent with time. In rape news, both reactions are consistent and maintain a strong positive correlation, meaning they increase and decrease together. Based on the theories of emotion, we quantitatively answer one research question: How do social media users react to rape with the five major Facebook reactions? The results suggest that users are more likely to express disdain toward rape and sympathy toward the victims using the angry button, along with the sad button. The primary aim of this study was to understand users’ different reaction patterns based on the five major Facebook reactions (i.e., love, haha, wow, sad, and angry). This study investigated 3.50 million Facebook reactions collected from 9,429 Bangladeshi news items about rape shared on social media from 2016 to 2021.
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