A number of modern technologies that can be hoped to…

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Pranjal PatilBegginer

Asked: July 21, 20242024-07-21T17:08:28+05:30 2024-07-21T17:08:28+05:30In: Science & Technology

Machine learning model selection

Suppose you want to work on a project which classifies social media comments as toxic and non-toxic, what data would you use for the model trainimg.

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Shivanjali S · Answer 1 · 2024-07-21T17:35:48+05:30

For training a model to classify social media comments as toxic and non-toxic, you would use the following types of data:

Labeled Comment Datasets: Use a dataset with comments or posts already labeled as “toxic” or “non-toxic.” Examples include the Jigsaw Toxic Comment Classification Challenge dataset or other publicly available datasets from platforms like Kaggle.
Feature Data: Extract features from the comments, such as:
- Textual content: The actual words and phrases used.
- Metadata: Information like the time of the comment, user profile details, or context.
Pre-Processed Text: Include cleaned and pre-processed versions of the text, with steps like tokenization, lowercasing, removing stop words, and stemming or lemmatization.
Contextual Data: Sometimes additional context, like previous comments or user history, can help improve model accuracy.
Diverse Samples: Ensure the dataset is diverse and representative of different languages, slang, and comment types to build a robust model.

Using a combination of these data types helps create a well-rounded model capable of accurately classifying comments in various contexts.