HATE CONTENT DETECTOR

Inspiration

to solve real world problems and try to contribute as much as I can.

What it does

       Social platforms large and small are struggling to keep their communities safe from hate speech, extremist content, harassment and misinformation. The prevalence of hateful or offensive language online has been growing rapidly in recent years, and the problem is now rampant. In some cases, toxic comments online have even resulted in real life violence 
      Summary – Hate speech and Content has been linked to a global increase in violence towards minorities, and other religions

How we built it

1.Got a Twitter dataset from Kaggle. 2.Cleaned the data using the tweet-preprocessor library and the regular expression library. 3.Splitted the training and the test data by 70/30 ratio. 4.Vectorized the tweets using the Count Vectorizer library. 5.Built a model using Support Vector Classifier. 6.Achieved approximately 95% accuracy

Challenges we ran into

basically, we don't know all the tech stack before starting the project. we learned while during hackathon and build with what we learned.

Accomplishments that we're proud of

The model created by us takes any random tweets as input And classifies them as tweets with hate content and tweets without hate content. We can report those hate content tweets to respected person , who wants details of those tweets. our model is built based on Support vector classification, We used tweet preprocessor library and regular expression Library and imported them into python code