MaNLP@SMM4H22: BERT for Classification of Twitter Posts
This is an incremental improvement for social media health mining applications, specifically targeting age classification in tweets.
The authors tackled the problem of classifying tweets self-reporting age into exact or non-exact age categories, achieving F1 scores of 0.80 and 0.81 in a shared task.
The reported work is our straightforward approach for the shared task Classification of tweets self-reporting age organized by the Social Media Mining for Health Applications (SMM4H) workshop. This literature describes the approach that was used to build a binary classification system, that classifies the tweets related to birthday posts into two classes namely, exact age(positive class) and non-exact age(negative class). We made two submissions with variations in the preprocessing of text which yielded F1 scores of 0.80 and 0.81 when evaluated by the organizers.