Abstract
In today's research domains, the automatic analysis in texts of emotion is of growing importance. Increasing social media popularity, people started sharing their views and thoughts through textual messages. At present, people prefer to reveal the ideal answer and social assistance, including in publicly organized topics such as child abuse (CA). The increasing number of abuse-related messages posted on social media has been significant in monitoring public well-being and support programs for the public health system and family care organizations. Hence in this paper, a machine learning-based text emotion analysis model (ML-TEAM) has been proposed to predict analyzing the emotions and preventive measures to reduce children's psychological abuse. The objective is to interpret the mental state that can identify the individual's emotional form from their text. Various characteristics are studied to assess the characteristics derived from predictive strength, such as psycholinguistic, textual and sentimental, and machine learning algorithms. Therefore, in classifying potential instances of harassment posts from the different user positions, a more predictive capacity of high precision results of 98.11% in the numerical solution.
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