Message Classification in Facebook learning group using ML

The objective of the project is Message Classification in facebook learning group using Machine Learning to Data Analysis.

Platform? ? ? ? ?: Python
Delivery? ? ? ? ? :? One Day
Support? ? ? ? ? : Online Demo with Explanation
Deliverables? : Project Files, Report and Presentation
SKU: PAN_ML_032 Categories: ,
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Message Classification in Facebook learning group using ML


Research on cyberbullying detection is gaining growing interest in latest years as each person sufferers and societies are substantially tormented by it. Moreover, ease of get admission to? social media systems which include Facebook, Instagram, Twitter, etc. has brought about an exponential boom withinside the mistreatment of human beings withinside the shape of hateful messages, bullying, sexism, racism, competitive content, harassment, poisonous remark etc. Thus there’s an in depth want to identify, manage and decrease the bullying contents unfold over social media sites, which has stimulated us to behavior this studies to automate the detection method of offensive language or cyberbullying. Our major intention is to construct unmarried and double ensemble-primarily based totally vote casting version to categorise the contents into? groups: ?offensive? or ?non-offensive?. For this purpose, we’ve got selected 4 gadget gaining knowledge of classifiers and 3 ensemble fashions with? specific characteristic extraction strategies blended with numerous n-gram evaluations

on a dataset extracted from Facebook. In our work, Logistic Regression and Bagging ensemble version classifier have accomplished for my part quality in detecting cyberbullying which has been outperformed via way of means of our proposed SLE and DLE vote casting classifiers. Our proposed SLE and DLE fashions yield the quality overall performance of 96% whilst TFIDF (Unigram) characteristic extraction is carried out with K-Fold go validation. Message Classification in Facebook learning group using ML


The increasing use of online platforms has created a powerful influence over people which enables them to express their views and ideas freely than ever before. Social media sites such as Twitter and Facebook has become an integral part of our day-to-day life due to their enormous popularity among people, particularly among teenagers. Certainly, the substantial increase in the usage of these platforms has also some negative consequences as well as these teenagers are often exposed to various behavioral and psychological threats


Cyberbullying in the form of influential social attacks is one of the potential sources of these threats. Moreover, social media platforms allow people to harness the option of being anonymous and hiding their self-identity, which can lead to misusing the technical features to satisfy their unkind deeds. Things also become worse when bullying occurs more frequently over time.


Comparing the performance of machine learning and deep learning algorithms classifying messages
Comparing the performance of machine learning and deep learning algorithms classifying messages


Cyberbullying or Offensive messages and posts over social media are continuously affecting individuals particularly teenagers and society and often lead to a series of consequences even suicidal thoughts among the victims. In this research, we have built two ensemble-based voting models to detect offensive or non-offensive texts. Our proposed model has outperformed all the independently applied ML algorithms and ensemble techniques.

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