Negators, modals, and degree adverbs can significantly affect the sentiment of the words they modify. Often, their impact is modeled with simple heuristics; although, recent work has shown that such heuristics do not capture the true sentiment of multi-word phrases. We created a dataset of phrases that include various negators, modals, and degree adverbs, as well as their combinations. Both the phrases and their constituent content words were annotated with real-valued scores of sentiment association. Using phrasal terms in the created dataset, we analyze the impact of individual modifiers and the average effect of the groups of modifiers on overall sentiment. We find that the effect of modifiers varies substantially among the members of the same group. Furthermore, each individual modifier can affect sentiment words in different ways. Therefore, solutions based on statistical learning seem more promising than fixed hand-crafted rules on the task of automatic sentiment prediction.

通过创建一个包含否定词、情态动词和程度副词以及它们的组合的短语数据集，并且使用标记了真实感情联想分数的短语及其成分内容词，分析了各个修饰语的影响和组合在情感分析中的平均效果，研究表明修饰语的影响在同一组中的成员之间差异显著，并且每个单独的修饰语可以以不同的方式影响情感词，因此基于统计学习的解决方案对于自动情感预测任务更具有前景。

否定词、情态动词和程度副词对情感组合的影响