Offensive Language Detection
We can describe Offensive Language Detection as identifying abusive behaviors, such as hate speech, offensive language, sexism, and racism, in any text-related conversation on digital platforms.
We can also refer to it as Hate Speech Detection, Abuse Detection, Flame, or Cyberbullying Detection.
Let us introduce the topics discussed in this blog:
- What is Offensive Language Detection?
- What are its Classifications?
- What are the Advantages of using this technology?
- How can Textrics help you detect offensive language efficiently?
1. What is Offensive Language Detection?
In recent years, with the increased use of social media platforms, human interactions are becoming rapid and informal at
the same time. Administrators of these platforms are using extensive methods to check inappropriate behavior and
language.
In almost any social community, we can find offensive language in text formats such as text messages, instant messages, social media messages, comments, message forums, and even online games.
As more user-generated content is delivered across social media, the data becomes too massive for manual filtering. This information overload on the internet requires intelligent systems that can identify potential risks automatically.
Naturally, automated methods to detect abusive language reliably are in high demand.
Offensive Language Detection involves the adequate detection of potentially harmful messages on the internet in large volumes, quickly and efficiently. Organizations and businesses, including government bodies, have also suggested using automated offensive language detection tools to detect abusive language.
This way, the administrators can take subsequent actions to remove such texts from public visibility faster.
Know more: Detect the objective of text through Intent Analysis
2. What are its classifications?
We have trained our model to classify textual data into two categories: Abusive and Non Abusive.
We had combined all abuse-related labels as “ABUSIVE” in the pre-processing stage and the remaining labels as “NON-ABUSIVE”.
3. What are the advantages of using this technology?
Recently, pattern recognition and machine learning algorithms are being used in various Natural Language Processing (NLP) applications.
Every day we have to deal with texts (emails or different types of messages) in which there are a variety of attacks and abusive phrases. Therefore, an automatic system for discriminating between standard texts and flames would save time and energy during browsing on the web and in our regular emails or text messages.
Such automated systems consist of features that carry out Offensive Language Detection functions based on several factors such as:
- The frequency of phrases that fall into one of the graded (weighted) flaming patterns (for each grade/weight separately);
- The frequency of graded/weighted words or phrases with abusive/extremist load in each grade;
- The highest grade (maximum weight) which occurs in a context;
- The normalized average of the graded/weighted words or phrases.
Therefore, Offensive Language Detection systems are incredibly straightforward, easy to use, fast and efficient. They can scan through large volumes of data, enabling smooth functions across industries and organizations.
4. How can Textrics help you detect Offensive Language efficiently?
Offensive language, hate speech, and bullying behavior is prevalent during textual communication happening online.
Offensive Language Detection effectively tackles such problems by identifying offense, aggression, and hate speech in user’s textual posts, comments, microblogs, etc. Social media platforms, analytics companies, and online communities had shown much interest and involvement in this field to cope with this problem by stopping its propagation in social media and its usage.
Nowadays, machine learning models are actively being deployed to detect the abusive language in an online environment. We implemented various kinds of neural network-based models such as CNN, RNN, and their variant models.
At Textrics, we have also used a pre-trained FastText representation for word-level features. We have trained our model on a training dataset by performing LSTM and Bi-LSTM.,
To learn more, check out our free demo.
Further Reading → Understand the customer needs through Free Online Sentiment Analysis