Facebook, as we all know, is such a happening place that sometimes it feels that people are more active on Facebook than they are in their real life.
People tend to put their status on Facebook thereby revealing, knowingly or unknowingly, more than what they were supposed to reveal. In fact, just by looking at someone’s Facebook updates one can identify when a person is feeling happier or when he is low in mood.
Data Science: It can be thought of as a process of uncovering the facts hidden behind the data by analyzing it in some or the other way. Data analytics helps us to analyze the data. If we want to analyze a given data, the first question that comes to our mind is “What is data, to begin with?”. E.g., if you are provided with the data that is capable of answering the following questions like how many times do a person smiles in a day? How many straights of the walk does he take every day? What is his salt intake on an average per day? How much junk he eats, how many hours does he sleep? Now, if you get this data for one full year and then analyze it, isn’t it seems easy to infer, that how healthy lifestyle a person is living?
Now, If something as simple and straight forward as number of walking straights you take per day, say something about your fitness level. What about the data you update on Facebook?
Assume if we take all the data that you have shared on Facebook from the past one month, status one, status two, status three, status four up to let’s say some hundred status messages that you have shared from the past one month by observing this what information can be inferred?
For example, if someone’s status was something like “life isn’t fair as I expected it to be”, don’t you think he is sounding slightly negative? Second example, assume someone’s status were to be something like this, “I am proud of my country”, irrespective of the tone in which one might talk the sentence I am proud of my country gives a positive connotation.
Now let us look at the third example, set your goals right! so it’s a very suggestive one-liner neither positive nor negative its neutral. Here, the sentiment of the first sentence shows that it is negative, second being positive the third being neutral. We told you three sentences and you realized that it was positive, negative and neutral. Is there any way we can use computer programs to predict what is the sentiment of a given sentence without any human intervention? Of what use will that be? It can be analyzed using the programming that whether a given sentence is positive, negative or neutral. If you have been asked to take one month’s data of a person which comprises of hundred status messages, and then analyze the sentiment of these sentences.Assumeeighty out of hundred where negative which means this person is undergoing some trauma in his life or isn’t in general happy person, on the contrary, what if ninety messages out of hundred where full of joy it means this person is very happy and satisfied in his life. what amount of these hundred status messages are positive how many of them are negative? And how many of them are neutral? If you are able to ask and answer this question we can say something about this person himself.
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