I am new to NLP. I have few 100 textual sentences (100 rows in dataframe) with an average word length of 10 in a sentence. I would like to know what interesting insights (simple descriptive to advanced) can be derived using NLP techniques. I don't intend to predict anything but analyze and get some interesting insights.
I have thought of the below items that can be done using sample data that I have.
Count the number of occurrences of each word in a sentence and finally find out the most frequently used (top) word and least used (bottom) word in the list of sentences that I have
Find the Entity in each sentence using NER. Which entity is discussed most in the sentences that I have?
Find which sentences are similar using textual similarity metrics.
I can identify the sentiment of the sentence
Can LDA be used to identify the topic of the sentence (which on average has 10 words) and my dataset itself has only 100 sentences?
What do you think is the use of creating syntactic/dependency trees? What can we infer through this? This might be useful for linguists, but can it help the layman end-users/business folks get some insight? Any simple explanation on this topic or directing me to resources would be helpful
I think we cannot summarize it because my sentences only contain 10 words on average
Can you help me with q5, q6, and q7?
Is there anything else that you think can be done? What more do you think can be done using.