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Precision trend plot for racist tweets across model classifiers and data sets. Balancing the data set improved the precision.
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In an age of social media, online forums, and chats, cyberbullying is a prevalent issue. On Twitter (now X), approximately 500 million tweets are shared per day (Antonakaki et.al., 2021). It is the job of the moderators to ensure these tweets follow standard community guidelines. However, the sheer number of tweets makes it difficult to sort manual...
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... can see from the table that less than 5% of tweets were misclassed and majority of the tweets were predicted correctly. Figure 4 shows the precision trend for racist tweets for various ML classifiers that we tried. The precision trend shows with data preprocessing and balancing the data set with over sampling methods improved the performance across all ML models, except Multinomial NB. Figure 5 show the recall metric trend for racist tweet class and we observe how balancing the data between the classes strongly improved the recall score. ...
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Abstract-The rapid development of Artificial Intelligence (AI) technology for artificial intelligence has become a comprehensive topic of social debate, especially in many social media nowadays. The purpose of this study is to analyze public sentiment about AI using tweet data collected through scraping comment data. A total of seven data records using AI-related keywords such as chatgpt, openai, and deepseek were processed along with NLP (natural language processing) technology. Before processing, it included text cleaning, lemmatization, and removing stop words. Mood analysis was performed using the Vader algorithm. The results showed that 47% of tweets were positive, 32% were neutral, and 21% were negative. The data visualization also shows the most frequently used words in AI-related discussions and the most active users. This study includes a general explanation of public perception of AI, opening up opportunities for further studies on the dynamics of public discourse in the digital age.
