On the other hand, we apply the PageRank algorithm to rank important words in each document.

Whereas, we apply the PageRank algorithm (Brin et al., 1998) for the issue, because the algorithm scores the centrality of a node in a graph, and important words should be regarded as having the centrality (Hassan et al., 2007).

ment for text classification, there are many studies which use the PageRank algorithm.

They apply topic-specific PageRank to a graph of both words and documents, and introduce Polarity PageRank , a new semi-supervised sentiment classifier that integrates lexicon induction with document classification.

As a study related to topic detection by important words obtained by the PageRank algorithm, Kubek et al.

In particular, (Hassan et al., 2007) shows that the PageRank score is more clear to rank important words rather than tfidf.

In this study, we refer to their method and use PageRank algorithm to decide important words.

We develop induction algorithms based on three distinct types of algorithmic framework that have been shown successful for the analogous task of sentiment lexicon induction: HITS & PageRank (§2.1), Label/Graph Propagation (§2.2), and Constraint Optimization via Integer Linear Programming (§2.3).

2.1 HITS & PageRank

(2011) explored the use of HITS (Kleinberg, 1999) and PageRank (Page et al., 1999) to induce the general connotation of words hinging on the linguistic phenomena of selectional preference and semantic prosody, i.e., connotative predicates influencing the connotation of their arguments.

We find that the use of label propagation alone [PRED-ARG (CP)] improves the performance substantially over the comparable graph construction with different graph analysis algorithms, in particular, HITS and PageRank approaches of Feng et al.

To construct each semantic signature, we use the iterative method for calculating topic-sensitive PageRank (Haveliwala, 2002).

The PageRank may then be computed using:

For our semantic signatures we used the UKB2 off-the-shelf implementation of topic-sensitive PageRank .

As our WSD system, we used UKB, a state-of-the-art knowledge-based WSD system that is based on the same topic-sensitive PageRank algorithm used by our approach.

When using the user graph as feature, we compute the authority score for each user with PageRank as shown in Equation 1.

PageRank Score: We employ the PageRank (Page et al., 1999) score of each URL as popularity score.

We compute the user’s authority score (AS) based on the link analysis algorithm PageRank: