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The proliferation of harmful content on social media affects a large part of the user community. Therefore, several approaches have emerged to control this phenomenon automatically. However, this is still a quite challenging task. In this paper, we explore the offensive language as a particular case of harmful content and focus our study in the analysis of keywords in available datasets composed of offensive tweets. Thus, we aim to identify relevant words in those datasets and analyze how they can affect model learning. For keyword extraction, we propose an unsupervised hybrid approach which combines the multi-head self-attention of BERT and a reasoning on a word graph. The attention mechanism allows to capture relationships among words in a context, while a language model is learned. Then, the relationships are used to generate a graph from what we identify the most relevant words by using the eigenvector centrality. Experiments were performed by means of two mechanisms. On the one hand, we used an information retrieval system to evaluate the impact of the keywords in recovering offensive tweets from a dataset. On the other hand, we evaluated a keyword-based model for offensive language detection. Results highlight some points to consider when training models with available datasets. [ABSTRACT FROM AUTHOR]

In recent years, there has been a tremendous increase in the amount of data generated from various sources including social media, news articles, and blogs. With the rise of social media platforms, people are expressing their opinions more freely than ever before. This has led to an explosion of data in the Indian Election domain, where people express their views on various political parties and candidates. In order to extract meaningful information from this vast amount of data, it is important to identify and extract relevant keywords and phrases. Keyword and phrase extraction is the process of automatically identifying important words and phrases from a piece of text. This process is crucial for various natural language processing tasks such as text mining, sentiment analysis, topic modeling, and text classification. In this research paper, we focus on the task of keyword and phrase extraction from Indian Election domain text. We aim to extract relevant keywords and phrases that are most commonly used in the context of Indian elections. This research is important as it can help in understanding the key issues and concerns of Indian voters during the election season. We use various natural language processing techniques and machine learning algorithms to extract keywords and phrases from a large corpus of Indian Election domain text. Our approach involves pre-processing the text, including tokenization, stop-word removal, stemming, and POS tagging. We then use various statistical and machine learning models to identify the most relevant keywords and phrases. [ABSTRACT FROM AUTHOR]