论文信息 - SZTE-NLP: Aspect level opinion mining exploiting syntactic cues

SZTE-NLP: Aspect level opinion mining exploiting syntactic cues

In this paper, we introduce our contributions to the SemEval-2014 Task 4 ‐ Aspect Based Sentiment Analysis (Pontiki et al., 2014) challenge. We participated in the aspect term polarity subtask where the goal was to classify opinions related to a given aspect into positive, negative, neutral or conflict classes. To solve this problem, we employed supervised machine learning techniques exploiting a rich feature set. Our feature templates exploited both phrase structure and dependency parses.

István Varga | Gábor Berend | Richárd Farkas | Viktor Hangya

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