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Abstract
Social media has become a new method of today’s communication in a new digitalize era. Children and adults have used social media a lot in interacting with others. Therefore social media has shifted conventional communication into digital one. This digital development on social media is a serious problem that must be faced because it has been found that there are more and more acts of cyberbullying. This act of cyberbullying can attack the psychic, causing depression up to suicide. The dangers of cyberbullying are troubling and cause concern to the community. Therefore, this study will analyze the sentiment on the comments contained on social media to find out the value of sentiment from comments on social media platforms. The comment data will be processed at the preprocessing stage, Term Frequency-Inverse Document Frequency (TF-IDF), and the Support Vector Machine (SVM) classification method. Comment data to be classified as 1500 data taken using crawling data through libraries in python programming and divided into 80% data training and 20% data testing. Based on the results of the test, the accuracy value is 93%, the precision value is 95%, and the recall value is 97%. In this research, a system model design is also carried out where the system can be integrated with the browser to open a user page on the classification of comments that have been input into the system.
Highlights
Social media has become a new method of today’s communication in a new digitalize era
People have but there are concerns about increased online activity been interacting through social media such as Facebook, that could lead to the onset of deliberate crime and Twitter, Myspace, and YouTube that are accessed harassment such as cyberbullying
From the rapid growth of social already very popular among everyone and the growing media, cyberbullying becomes one of the serious popularity of social media platforms is increasing problems in social networks, especially for teenagers cyberbullying that occurs through social media [8], [9]
Summary
1 1 0 commentary words become a language that the system can read. Table 4 is the result of word-replacing. 5. 5. Stopword Removal This stage is carried out to select words that are not important in the comment. Stopword Removal This stage is carried out to select words that are not important in the comment The result of this stopword is meaningful commentary words in the content of the. In the preprocessing stage several stages are done, namely case folding, data cleansing, tokenizing, word replacing, stop word removal, and stemming [24]–[26]. The process of stemming is changing the commentary data which adds to the basic words. Stemming stages can be seen in the Table 4. Data cleansing, tokenizing, word replacing, stopword removal, and stemming stages are used as preprocessing stages. The results of which can be seen in the Table 5
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