IMPLEMENTASI APLIKASI SENTIMENT TEXT BERBASIS MEDIA SOSIAL DENGAN METODE SUPPORT VECTOR MACHINE (SVM) - Mikkel Septiano Budi Wasito (Jurnal Informatika dan Bisnis, 2018)
The development of the world of information technology is very rapid cause of the rapid information from various main sources of cyberspace. Sources of information not only can be obtained from electronic media and mass media but also social media media. Acquisition of information in social media is currently still too free plus not knowing the origin of information / anonymous causes of the general public information. It can have a negative impact on society. On the occurrence of this is the purpose of this research, namely to create a social media that has a sentiment analysis which will analyze the tweets that will be shared by the user. Social media is designed and created using HTML, C # ASP, Javascript, and SQL Server languages, using the .NET framework, and results in web application form in the browser. Social media also uses web API text analysis in analyzing the tweet sentence. The collection of information and user needs is done by trying some social media references such as Twitter, Secret, etc. to add the flow and features of such social media as well as reading books, journals, web pages as a supporting reference in building social media application web. Activity diagrams, entity relationship diagrams, and class diagrams. Social media created aimed at social media users in order to reduce negative information generated from social media users who need anonymous and able to provide results analysis tweetword that has a negative and positive meaning. The conclusion that can be gained from the making of social media is the availability of an information technology facilities to exchange positive thoughts and people can feel a sense of security and comfort in obtaining information from social media. For further development, social media is expected to obtain not only text information and can add images and video admin functionality to track social media user data. Keywords : Social Media, Sentiment, API