DEL ab@ IIITSM at ICON -2021 Shared Task: Identification of Aggression and Biasness Using Decision Tree (2021)

Abstract: This paper presents our system description on participation in ICON-2021 Shared Task sub-task 1 on multilingual gender-biased and communal language identification as team name: DELab@IIITSM. We have participated in two language-specific Meitei, Hindi, and one multi-lingualMeitei, Hindi, and Bangla with English code-mixed languages identification task. Our method includes well design pre-processing phase based on the dataset, the frequency-based feature extraction technique TF-IDF which creates the feature vector for each instance using(Decision Tree). We obtained weights are 0.629, 0.625, and 0.632 as the overall micro F1 score for the Hindi, Meitei, and multilingual datasets.

## introduction the present scenario of social media has opened great opportunities @xcite in natural language processing. different social media platforms provide users to express/deliver its opinion exclusively. the post or comments made over it can be affectionate, sarcastic, aggressive, bias, etc @xcite . its impact is highly immense, which can lead to serious problem @xcite . understanding and analyzing different topics has become an important area in today's world. it allows researchers with high exposure to various topics, which add growth in the field and to society. usage popularity of such platforms extensively implies growth in data availability. machine learning approaches have gained their recognition @xcite and played as back-boned in various experiments over social media content analysis. this experiment is based on the icon2021 shared task over-identification of aggression and bias related to gender and communal (particularly first subtask). it has provided separate hindi, meitei and bangla and multilingual dataset combination of all the separate dataset with english code-mixed for the task. each dataset consists of 3 different classes, namely aggressive, gender bias, and communal bias. the experiment aims to identify classes and their intersectionality among them. our model is based on frequency-based feature extraction technique (tfidf @xcite ) with hierarchical classifier (decision tree) @xcite . the obtained accuracy based on micro f1 score is 0.629, 0.625, and 0.632 for the hindi, meitei and multi dataset, and this shared task ranking is based on obtaining instance f1 score. our experiment placed rank at 3rd (hindi), 2nd (meitei), and 4th (multi) with instance f1 score as 0.263, 0.267, and 0.258 respectively for the different datasets. the rest of the paper is assembled in different sections. section 2 provides a survey made upon social media content to identify aggression and bias and methodologies implemented. later section 3, describe the details of the experiment performed over the shared task, begins with dataset description, technique and model used, and the result with error analysis obtained over this experiment. last section 4 draws the conclusion and further scope suggested towards the better outcome of the topic. ## literature survey aggression, gender and communal bias identification are the new research topics in the field of nlp. few specific and related work in this topic make use of feature extraction techniques like bow @xcite , dictionary @xcite , word and character level ngram (pérez and luque, 2019) and lexicons based @xcite ann-based advance feature embedding techniques such as glove (kumari and singh, 2020; @xcite , fast-text @xcite @xcite , word2vec @xcite and bert @xcite @xcite are also seen reported. multi-lingual model on aggressive identification using frequency based feature extraction @xcite @xcite has shown improvement over the earlier methods. above mentioned techniques observed in gender bias classification @xcite @xcite . communal bias text identification is another challenging and new area under nlp. there is comparatively less work related to communal bias text identification, related work includes @xcite @xcite . as mentioned earlier, machine learning algorithm plays a promising role in different classification problems. the data structure and multiclass property of the dataset pulls the attention of hierarchical tree based classification. decision tree classifier is widely employed with good performance over multiclass problem @xcite @xcite . relatively, its implementation over area of text classification like aggression, hatespeech and gender-bias is seen in @xcite @xcite and these techniques outperformed in many other text classification task @xcite @xcite . ## system architecture this section discusses the detail of the used dataset provided by the shared task organizer and its implementation. ## conclusion related to the icon-2021 shared task, we participated in subtask 1 on multilingual genderbiased and communal language identification for the hindi, meitei, and multilingual datasets. our system is built upon the tfidf feature technique with decision tree as a classifier and obtained an f1-score of 0.629, 0625, and 0.632. in the future, we aim to build the multilingual model by embedding relative lexicon and enhancing frequencybased features extensively.