Keyword Extraction from Tweets Using Weighted Graph
One of the most important tasks of sentiment analysis of twitter contents is automatic keyword extraction. Vector Space Model (VSM) is one of the most well known keyword extraction techniques; however it has some limitation such as scalability and sparsity. Graph based keyword extraction approach is used to overcome those limitations. This paper proposes an unsupervised graph based keyword extraction method, called Keyword from Weighted Graph (KWG) which uses Node Edge (NE) rank centrality measure to calculate the importance of nodes closeness centrality measure to break the ties among the nodes. The proposed method is validated with two datasets: Uri Attack, and American Election. From the experimental results it is observed that the performances of the proposed method outperform the eigen vector centrality and the textrank centrality measures. The performances are shown in terms of precision, recall, and F measure.