INCOGNITO'S DM

Inspiration

With the rise of natural disasters exacerbated by climate change, governments and agencies struggle to coordinate effective relief programs. Artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) can help. Twitter has become an important communication channel in times of emergency. The ubiquitousness of smartphones enables people to announce an emergency they’re observing in real-time. Also, Twitter has been used as an effective tool to express their feelings about the disaster event, which can be very helpful for the governing bodies to understand the reactions of the masses and take appropriate actions. During the period of disaster response, a large number of users posted information like disaster damage reports and disaster preparedness situations, making Twitter an essential social media for updating and accessing data. Mining sentimental data efficiently will better understand the disaster response timely and easily

What it does

so our model decides the sentiments of the tweets are negative or positive which is basically called TWITTER SENTIMENTS ANALYSER

How we built it

we made our model in the Python language using a jupyter application using databases given by the hackathon department

Challenges we ran into

we ran into many challenges like classification and analyzing the data weren't easy tasks to do initially. reading so many databases in less period of time was not an easy task. we need to import many libraries like NumPy, pandas, etc.

Accomplishments that we're proud of

we are in the second year of a bachelor of technology in electronics and telecommunications so by taking the out-of-the-box step we participated in this hackathon. so it was not easy for us to this task or make a model in a less given time