Getting started with “NLP” !!! Let's analyze your sentiments….
Hello Readers !! In this blog, let's analyze your sentiment are you a negative or a positive person !!!
Today I want to share what I have learned from an Internship that I finished last month basically It was a research internship where I gained lots of experience in data science and its branches. So without any further wait let's starts with today's topic which is NLP some of You might have heard of it but I think I should start with the basic and introduce it to all. So it's going to be a blog where I introduce the term NLP and its use cases in the real world.
“NLP” → Natural Language Processing is one of the fields of data science that is growing exponentially in today's world the term says “Natural Language” means the language we speak in day-to-day life for me it’s my mother tongue, “Hindi” and “English” and we simply train our model by feeding these sentence to make our machines learn about sentiment, sarcasm, Emotion, etc.
Does this thing amaze you? For me, This is like How? How it's possible to make my machine know In what sense I am saying things? as Humans are too complicated the same sentence sounds different just by adding a word the positive sentence sounds negative just by removing or adding a word. Humans can understand this well but machines How?
Here NLP comes into play. let's discuss one of my projects In which I build a machine learning model which let you know what reviewers and critics think about a movie that is already released or about to release. I used an already available dataset (IMDB 50k reviews dataset available on Kaggle)to train my model and as this is a basic blog so I’ll not discuss how I make this model and the other technical details of the model but I’ll show you the working result of this model.
So I’ll take two Bollywood movies by the same actor Aamir khan, first Is one of the best and my favorite “3 Idiots” and the second is the newly released “Laal Singh chadda” let's test our model to give the expected result by analyzing the reviews on IMDB to tell what reviewers thought about these two movies.
Let’s see the result that my model gives for this movie
Result for 3 idiots movie → 90 percent of reviewers gave a positive response to the movie and I don’t know who are these 10 percent people are they even from earth ?? joke aside but I think my model just works fine as it's giving satisfying results as we all know the success of “3 idiots” movie. now let's analyze the result of our second movie.
As we can see that the Reviers mostly give negative reviews toward the movie which we can see by the box office earnings of the movie So The model is working perfectly fine. Bollywood’s golden age is depleting now what's your thought let me know in the comment ………….
But why one should want the sentiment of others related to any topic? It's simple it has many use cases for instance what if the director and producers already know what people think before releasing a movie to an area they may not release the movie on such a large scale maybe they can use OTTs for this also they also can predict how much box office collection the movie is going to make.
If we think there is much other use for NLP what if we can analyze tweets to predict the election result, the success rate of a brand, and also the reaction of the public on a matter, for example, the farmer protest of last year, detection of fake news as we know the fake news spread on WhatsApp, Instagram, and other social media is one of the major problems, etc.
Not only this did you have noticed while typing an email the words or grammar get auto-correct that's also one of the major contributions of NLP also there are sites that give a summary of a topic from all over the internet you have searched that is also a major use case of NLP. So NLP is one of the major fields in which much more research is to be done with this, I want to end this blog Thanks for reading this Blog …… if you want another blog about how I made that model please let me know in the comment section…..✌️
So by writing this blog I have done…………
