The increasing growth of social media provides us with an instant opportunity to be informed of the opinions of a large number of politically active individuals in real-time. We can get an overall idea of the ideologies of these individuals on governmental issues by analyzing the social media texts. Nowadays, different kinds of news websites and popular social media such as Facebook, YouTube, Instagram, etc. are the most popular means of communication for the mass population. So the political perception of the users toward different parties in the country is reflected in the data collected from these social sites. In this work, we have extracted three types of features, such as the stylometric feature, the word-embedding feature, and the TF-IDF feature. Traditional machine learning classifiers and deep learning models are employed to identify political ideology from the text. We have compared our methodology with the research work in different languages. Among them, the word embedding feature with LSTM outperforms all other models with 88.28% accuracy.

翻译：社交媒体的快速增长为我们提供了实时了解大量政治活跃个体观点的即时机会。通过分析社交媒体文本，我们可以对这些个体在政府议题上的意识形态形成整体认知。当前，各类新闻网站及Facebook、YouTube、Instagram等流行社交媒体已成为大众最常用的传播媒介。因此，用户对国家不同政党的政治认知也反映在从这些社交平台收集的数据中。本研究提取了三类特征：文体计量特征、词嵌入特征和TF-IDF特征。我们采用传统机器学习分类器与深度学习模型从文本中识别政治意识形态，并将本方法与针对不同语言的研究工作进行了比较。其中，结合LSTM的词嵌入特征以88.28%的准确率优于所有其他模型。