The second one is a graph-based method, which incorporates topic and sentiment information, as well as additional information about sentence-to-sentence relations extracted based on dialogue structure.

In particular, we find that incorporating dialogue structure in the graph-based method contributes to the improved system performance.

In the graph-based method, the best parameters are Asim = 0,)\adj = 0.3,Arel = 0.4,Asent = 0.3.

This is different from graph-based summarization systems for text domains.

When compared to abstractive reference summaries, the graph-based method is slightly better.

widely used in extractive summarization: sentence-ranking and graph-based methods.

Furthermore, in the graph-based method, we propose to better incorporate the dialogue structure information in the graph in order to select salient summary utterances.

The second one is a graph-based method, which incorporates the dialogue structure in ranking.

4.2 Graph-based Summarization

Graph-based methods have been widely used in document summarization.

Graph-based optimization as the third step to further boost the performance by taking the related tweets into consideration.

In addition, different from previous work using only information on the current tweet for sentiment classification, we propose to take the related tweets of the current tweet into consideration by utilizing graph-based optimization.

According to the experimental results, the graph-based optimization significantly improves the performance.

6.4 Evaluation of Graph-based Optimization

For these tweets, our graph-based optimization approach will have no effect.

Target-dependent sentiment classifier +Graph-based optimization

If we consider that the sentiment of a tweet only depends on its content and immediate neighbors, we can leverage a graph-based method for sentiment classification of tweets.

We use graph-based label propagation for cross-lingual knowledge transfer and use the projected labels as features in an unsupervised model (Berg—Kirkpatrick et al., 2010).

We have shown the efficacy of graph-based label propagation for projecting part-of-speech information across languages.

To provide a thorough analysis, we evaluated three baselines and two oracles in addition to two variants of our graph-based approach.

We tried two versions of our graph-based approach:

In graph-based learning approaches one constructs a graph whose vertices are labeled and unlabeled examples, and whose weighted edges encode the degree to which the examples they link have the same label (Zhu et al., 2003).

Note, however, that it would be possible to use our graph-based framework also for completely unsupervised POS induction in both languages, similar to Snyder et al.

First, we use a novel graph-based framework for projecting syntactic information across language boundaries.