Factor Analysis in Text Mining task

Question

I was faced with the task of determining the topics of big text massive. For example you have 1 million any text phrases or sentences. I want factorize the main topic from this massive. The ordinary factor analysis works with continuous data. Is there analog of factor analyze, but for text mining tasks? In ideal factorize big text massive, then select any factors (semantic core) instance F1 f2 topic 1 topic 2 topic 3 topic 4
or maybe you can help me find the greatest way to decide my task. I.e. i want understand What are the main topics of interesе me people

Answer 1

I did this for Chinese Language text. Guessing yours is in English? The methods I used may work for you too. First of all, you need to define the concept of "topic", that could be a family of related key words. Second, you need to have a database that has the synonyms and antonyms, and the form changes like "calculate, -ing, -ed" etc. Then you count the frequency of the usage of same word, sorting them by category, etc. things like that. You need to take out words like (of, with, etc.) Hope it helps.

For example, in the text of your question, there are about 120 words. The counting results shows topic (7), factor (6), text (5), task (4), analysis (3). Others are not in high frequency. If you get the above data from a computer, and you did not read the text, you may guess that it is about "an analysis task of topic or factor of text".