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سند علمی

Semi-supervised vs. Cross-domain Graphs for Sentiment Analysis

Natalia Ponomareva, Mike Thelwall

چکیده

The lack of labeled data always poses challenges for tasks where machine learning is involved. Semi-supervised and cross-domain approaches represent the most common ways to overcome this difficulty. Graph-based algorithms have been widely studied during the last decade and have proved to be very effective at solving the data limitation problem. This paper explores one of the most popular stateof-the-art graph-based algorithms label propagation, together with its modifications previously applied to sentiment classification. We study the impact of modified graph structure and parameter variations and compare the performance of graph-based algorithms in cross-domain and semi-supervised settings. The results provide a strategy for selecting the most favourable algorithm and learning paradigm on the basis of the available labeled and unlabeled data.

اسناد مشابه

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