some algorithms to identify of a pattern in text data
Since you don't seem to have any annotated data, the best you can do is probably this:


Optional first step: remove stop words (there are many such lists available, for example https://pythonspot.com/nltk-stop-words/)
Calculate the Inverse Document Frequency for every word in the vocabulary. This is intended to measure the importance of a word based on how often it's used, in the sense that a word used in fewer sentences makes it more important for these sentences.
Calculate TF-IDF for every word in a sentence, then rank the words by their TF-IDF weights. In general the top ones are supposed to be the most relevant. Optionally you can decide to select the top N words as keywords.