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Mining Semantic Patterns for Sentiment Analysis of Product Reviews

机译：挖掘语义模式进行产品评论情感分析

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A central challenge in building sentiment classifiers using machine learning approach is the generation of discriminative features that allow sentiment to be implied. Researchers have made significant progress with various features such as n-grams, sentiment shifters, and lexicon features. However, the potential of semantics-based features in sentiment classification has not been fully explored. By integrating PropBank-based semantic parsing and class association rule (CAR) mining, this study aims to mine patterns of semantic labels from domain corpus for sentence-level sentiment analysis of product reviews. With the features generated from the semantic patterns, the F-score of the sentiment classifier was boosted to 82.31 % at minimum confidence level of 0.75, which not only indicated a statistically significant improvement over the baseline classifier with unigram and negation features (F-score = 73.93%) but also surpassed the best performance obtained with other classifiers trained on generic lexicon features (F-score = 76.25%) and domain-specific lexicon features (F-score = 78.91%).
机译：使用机器学习方法建立情感分类器的一个主要挑战是产生区分性特征，这些特征允许隐含情感。研究人员已经在诸如n-gram，情感转移器和词典功能等各种功能方面取得了重大进展。但是，尚未充分探索基于语义的特征在情感分类中的潜力。通过集成基于PropBank的语义解析和类关联规则（CAR）挖掘，本研究旨在挖掘域语料库中的语义标签模式，以进行产品评论的句子级情感分析。利用语义模式生成的特征，在0.75的最小置信度下，情感分类器的F分数提高到82.31％，这不仅表明相对于具有单字和否定特征的基线分类器（F分数）在统计学上有显着改善= 73.93％），但也超过了在通用词典特征（F分数= 76.25％）和特定领域词典特征（F分数= 78.91％）上训练的其他分类器获得的最佳性能。

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