Two-Stage Classifier for Campaign Negativity Detection using Axis
  Embeddings: A Case Study on Tweets of Political Users during 2021
  Presidential Election in Iran
Fatemeh Rajabi Ali Mohades
In elections around the world, the candidates may turn their campaigns toward
negativity due to the prospect of failure and time pressure. In the digital
age, social media platforms such as Twitter are rich sources of political
discourse. Therefore, despite the large amount of data that is published on
Twitter, the automatic system for campaign negativity detection can play an
essential role in understanding the strategy of candidates and parties in their
campaigns. In this paper, we propose a hybrid model for detecting campaign
negativity consisting of a two-stage classifier that combines the strengths of
two machine learning models. Here, we have collected Persian tweets from 50
political users, including candidates and government officials. Then we
annotated 5,100 of them that were published during the year before the 2021
presidential election in Iran. In the proposed model, first, the required
datasets of two classifiers based on the cosine similarity of tweet embeddings
with axis embeddings (which are the average of embedding in positive and
negative classes of tweets) from the training set (85\%) are made, and then
these datasets are considered the training set of the two classifiers in the
hybrid model. Finally, our best model (RF-RF) was able to achieve 79\% for the
macro F1 score and 82\% for the weighted F1 score. By running the best model on
the rest of the tweets of 50 political users that were published one year
before the election and with the help of statistical models, we find that the
publication of a tweet by a candidate has nothing to do with the negativity of
that tweet, and the presence of the names of political persons and political
organizations in the tweet is directly related to its negativity.