A sentiment lexicon is a list of expressions annotated according to affect categories such as positive, negative, anger and fear. Lexicons are widely used in sentiment classification of tweets, especially when labeled messages are scarce. Sentiment lexicons are prone to obsolescence due to: 1) the arrival of new sentiment-conveying expressions such as #trumpwall and #PrayForParis and 2) temporal changes in sentiment patterns of words (e.g., a scandal associated with an entity). In this paper, we propose a methodology for automatically inducing continuously updated sentiment lexicons from Twitter streams by training incremental word sentiment classifiers from time-evolving distributional word vectors. We experiment with various sketching techniques for efficiently building incremental word context matrices and study how the lexicon adapts to drastic changes in the sentiment pattern. Change is simulated by randomly picking some words from a testing partition of words and swapping their context with the context of words exhibiting the opposite sentiment. Our experimental results show that our approach allows for successfully tracking of the sentiment of words over time even when drastic change is induced.

情感词典是根据情感类别（例如正面，负面，愤怒和恐惧）进行注释的表达方式列表。词汇词典在推文的情感分类中被广泛使用，尤其是当标签消息稀缺时。由于以下原因，情感词典容易过时：1）新的情感表达方式如#trumpwall和#PrayForParis的到来； 2）单词情感模式的时间变化（例如与实体相关的丑闻）。在本文中，我们提出了一种方法，该方法可以通过训练随时间变化的分布词向量中的增量词情感分类器，从Twitter流中自动诱导出持续更新的情感词典。我们尝试了各种素描技术，以有效地建立增量单词上下文矩阵，并研究词典如何适应情绪模式的急剧变化。通过从单词的测试分区中随机选择一些单词并将其上下文与表现出相反情感的单词上下文进行交换来模拟变化。我们的实验结果表明，即使在引起剧烈变化的情况下，我们的方法也可以随着时间的推移成功跟踪单词的情感。