How to extract most occuring words based on month & what tool to use?
If you are new to data science and data munging, this could be kind of a tricky task, but a good one to get your feet wet.  Many programming languages have the capability to do this (R, Python, Matlab, etc.).  I use R primarily, so I'll give you a brief heuristic for how I'd approach the task in R.  Perhaps looking into these steps will get you started.


Install R
Install some packages that will help you along ('tm' for text mining,'dplyr' for cleaning/organizing your data, and perhaps also 'lubridate' for working with dates/times)
Read in your data from your source, be it a text file, spreadsheet, or some database (if a database, you'll have to conquer connecting R to said database too)
You want to do a word frequency analysis for each month. How you accomplish this will depend on how the data is organized, which I do not know, but it would involve first rounding all your dates to month (using lubridate's 'floor_date()' function is one way), then parsing the text for each month into a corpus that can be analyzed (using package tm).
Finally, for each month I would make a table counting words, sorting by frequency.  That would give you, for each month, the top 'trending' words. To discount words like 'the' and 'a', I might also use some of the tools in the 'tm' package to clean things up.
Note that in #5 I said 'words', not 'terms'.  If you want to account for terms consisting of > 1 words, you'll have to 'tokenize' them, but that's beyond the scope of this very brief intro.


As with many data science tasks, there are many ways to attack this; the above is but one of many possibilities.

Hope that helps.