Keyword extraction is a crucial process in text mining. The extraction of
keywords with respective contextual events in Twitter data is a big challenge.
The challenging issues are mainly because of the informality in the language
used. The use of misspelled words, acronyms, and ambiguous terms causes
informality. The extraction of keywords with informal language in current
systems is pattern based or event based. In this paper, contextual keywords are
extracted using thematic events with the help of data association. The thematic
context for events is identified using the uncertainty principle in the
proposed system. The thematic contexts are weighed with the help of vectors
called thematic context vectors which signifies the event as certain or
uncertain. The system is tested on the Twitter COVID-19 dataset and proves to
be effective. The system extracts event-specific thematic context vectors from
the test dataset and ranks them. The extracted thematic context vectors are
used for the clustering of contextual thematic vectors which improves the
silhouette coefficient by 0.5% than state of art methods namely TF and TF-IDF.
The thematic context vector can be used in other applications like
Cyberbullying, sarcasm detection, figurative language detection, etc.

该研究在提取 Twitter 上文本的关键词时，使用主题事件及数据关联来发现主题上下文，并使用主题上下文向量对其权重进行量化，最终对上下文主题向量进行聚类从而得出关键词。实验结果显示该系统较现有的基于 TF 和 TF-IDF 的方法在聚类结果上有 0.5% 的提升，并可在其他文本分析应用中使用。