CMHE-AN: Code mixed hybrid embedding based attention network for aggression identification in hindi english code-mixed text.

The widespread growth in social media platforms provides a plethora of opportunities to enhance interaction and bring awareness about recent activities happening across the countries. Many people use social media to share their thoughts and opinions on societal and political issues. Nonetheless, some individuals misuse these platforms by posting toxic, hostile, and insulting comments. Hence, detecting and controlling such content at its earliest stage is crucial since its spread can harm social relations and negatively impact a person's life. In current scenarios, social media text consisting non-English languages is increasing due to active participation from multilingual societies. Of several non-English languages, Hindi English code-mixed is more prevalent in India. Most of the previous work to detect cyber aggression concentrates on English texts; therefore, there is high scope left to work on other languages such as Hindi English code-mixed. This paper has proposed a code-mixed hybrid embedding (CMHE) at the character and word level to capture similarly spelled and contextually related words. Furthermore, proposed embedding contributes significantly to the reduction of out of vocabulary words and capture words having similar polarity. After this, a deep learning framework based on CMHE, and a self-attention mechanism is proposed to retrieve significant features for classification. To evaluate proposed model, experiments were performed with two publicly available datasets: TRAC 2-2020 Hindi English code-mixed dataset (77.54% accuracy, 77.09% weighted average f1 score) and hate speech dataset (75.23% accuracy, 73.34% weighted average f1 score). The attained experimental results validate the effectiveness of proposed approach against the state-of-the-art. [ABSTRACT FROM AUTHOR]