Probably a post which will be interesting to many of us. 15000 Tweets were collected that contained the phrase "women want". What do women value most when it comes to how they want to feel? What do women really love? How important is for women to feel special? And finally, can Tweets really tell us this information by applying Text Mining techniques to them?
Normally at this point i would describe technical details such as how i pre-processed Tweets and the problems i ran to while trying to analyze this information. Thanks to @nathalief i was advised to focus on giving information not only about what women want but also on their feelings.
First let's see the results from Tweets that apart the phrase "women want" they also contain words such as "feel, feeling, feels, felt" in them. The following chart shows what words where frequently found in these Tweets (and thus what feelings a woman wants to experience) :
So it appears that one of the first priorities in terms of how women want to feel is security (shown as safe and secure in the chart). Notice how important for women is also to feel special and to feel that someone loves them (words love, loved, like).
How about words that frequently occur with the word "Love" :
It appears that women want "to love and to be loved" with "respect", "affection" and "sex" coming next.
Surely there must be men that also give their opinions on "what women want" within these Tweets. Quite possibly many guys would say that "women just love money". In order to capture those who believe that women want money, let's see which words occur frequently with the word "money" within these Tweets :
Notice how the landscape of keywords changes here : Apart from "love", "secure" and "hurt" we see words labeled as "censored" (for obvious reasons), "shoes" and "future" : these words communicate a more materialistic and logical point of view on what women want. Unfortunately for this analysis there was no way to identify which Tweets were originated from women and which Tweets originated from men. Also at the time these tweets were collected a specific Re-Tweet was about a more 'materialistic' profile of women (words multiple,
and shoes). I decided to keep this re-tweet in the data that was analyzed because i felt that since this tweet was heavily re-tweeted then it was also liked by a large audience.
Perhaps these results show once again that "Men are from Mars and Women are from Venus"