| dc.description.abstract | Cyber bullying has rapidly increased in
the past few years with the growth of social
media usage and the COVID-19 pandemic. This
study uses a dataset of 65000 tweets , splitting
them into training and testing sets. Data preprocessing
was done using feature engineering
methods such as vectorizing, and Bag of Words to
prepare data to test machine learning models or
classifiers to build a model. Five different
classifiers were tested with dataset and Naïve
Bayes Model and linear support vector
classification model provided the best accuracy
and prediction times in sequence. The Sentiment
Analysis System was built using Naïve Bayes
Model and it is deployed to the web interface
using Flask to get user input and predict
sentiment in the three key aspects of negative,
positive and neutral. System tested with user
inputs and gained accurate sentiment Scores
(comment: “listen to my most beautiful friend
singing with her beautiful voice” Scores:
Compound- 0.97 Neutral – 0.166 Positive – 0.834
Negative – 0.0) with three key aspects. The aim of
this research work is to utilize man-made
consciousness at a specific level to pre-empt
exploitation by recognizing the riskiest clients
and accounts. | en_US |
