Political Ideology Detection Using Recursive Neural Networks

Abstract

An individual's words often reveal their political ideology. Existing automated techniques to identify ideology from text focus on bags of words or wordlists, ignoring syntax. Taking inspiration from recent work in sentiment analysis that successfully models the compositional aspect of language, we apply a recursive neural network (RNN) framework to the task of identifying the political position evinced by a sentence. To show the importance of modeling subsentential elements, we crowdsource political annotations at a phrase and sentence level. Our model outperforms existing models on our newly annotated dataset and an existing dataset.

A Neural Network for Factoid Question Answering over Paragraphs

“…Because there are a limited number of possible answers, we can view this as a multi-class classification task. While a softmax layer over every node in the tree could predict answers (Socher et al, 2011;Iyyer et al, 2014) To take advantage of this observation, we depart from Socher et al (2014) by training both the answers and questions jointly in a single model, rather than training each separately and holding embeddings fixed during dt-rnn training. This method cannot be applied to the multimodal text-to-image mapping problem because text captions by definition are made up of words and thus cannot include images; in our case, however, question text can and frequently does include answer text.…”

Ideological Phrase Indicators for Classification of Political Discourse Framing on Twitter

“…Important to the language analysis of our work, Tan et al (2014) have shown how wording choices can affect message propagation on Twitter. The study of political sentiment analysis (Pla and Hurtado, 2014;Bakliwal et al, 2013), ideology measurement and prediction (Iyyer et al, 2014;Bamman and Smith, 2015;Sim et al, 2013;Djemili et al, 2014), policies (Nguyen et al, 2015), voting patterns (Gerrish and Blei, 2012), and polls based on Twitter political sentiment (Bermingham and Smeaton, 2011;O'Connor et al, 2010;Tumasjan et al, 2010) are also related to the study of framing on Twitter. Boydstun et al (2014).…”

PLN-PUCRS at EmoInt-2017: Psycholinguistic features for emotion intensity prediction in tweets

“…Mohtasseb and Ahmed (2009) use psychological features to find online diaries in blogs. Iyyer et al (2014) LIWC Psycholinguistic features are also used to define the writer personality, as Poria et al (2013) shown in their work. Besides, it can be used to identify mental issues in online forum communities (Cohan et al, 2016).…”