Determination of quality television programmes based on sentiment analysis on Twitter

Public sentiment from social media like Twitter can be used as one of the indicators to determine the quality of TV Programmes. In this study, we implemented information extraction on Twitter by using sentiment analysis method to assess the quality of TV Programmes. The first stage of this study is pre-processing which consists of cleansing, case folding, tokenizing, stop-word removal, stemming, and redundancy filtering. The next stage is weighting process for every single word by using TF-IDF method. The last step of this study is the sentiment classification process which is divided into three sentiment category which is positive, negative and neutral. We classify the TV programmes into several categories such as news, children, or films/soap operas. We implemented an improved k-nearest neighbor method in classification 4000 twitter status, for four biggest TV stations in Indonesia, with ratio 70% data for training and 30% of data for the testing process. The result obtained from this research generated the highest accuracy with k=10 as big as 90%.