Question: What dataset do they use for their experiments?
Evidence: The ideological leanings of a person within the left-right political spectrum are often reflected by how one feels about different topics and by means of preferences among various choices on particular issues. For example, a left-leaning person would prefer nationalization and state control of public services (such as healthcare) where privatization would be often preferred by people that lean towards the right. Likewise, a left-leaning person would often be supportive of immigration and will often talk about immigration in a positive manner citing examples of benefits of immigration on a country's economy. A right-leaning person, on the other hand, will often have a negative opinion about immigration.
Most of the existing works on political ideology detection from text have focused on utilizing bag-of-words and other syntactic features to capture variations in language use BIBREF0 , BIBREF1 , BIBREF2 . We propose an alternative mechanism for political ideology detection based on sentiment analysis. We posit that adherents of a political ideology generally have similar sentiment toward specific topics (for example, right wing followers are often positive towards free markets, lower tax rates, etc.) and thus, a political ideology can be represented by a characteristic sentiment distribution over different topics (Section SECREF3 ). This topic-specific sentiment representation of a political ideology can then be used for automatic ideology detection by comparing the topic-specific sentiments as expressed by the content in a document (news article, magazine article, collection of social media posts by a user, utterances in a conversation, etc.).
In order to validate our hypothesis, we consider exploiting the sentiment information towards topics from archives of political debates to build a model for identifying political orientation of speakers as one of right or left leaning, which corresponds to republicans and democrats respectively, within the context of US politics. This is inspired by our observation that the political leanings of debators are often expressed in debates by way of speakers' sentiments towards particular topics. Parliamentary or Senate debates often bring the ideological differences to the centre stage, though somewhat indirectly. Heated debates in such forums tend to focus on the choices proposed by the executive that are in sharp conflict with the preference structure of the opposition members. Due to this inherent tendency of parliamentary debates to focus on topics of disagreement, the sentiments exposited in debates hold valuable cues to identify the political orientation of the participants.
We develop a simple classification model that uses a topic-specific sentiment summarization for republican and democrat speeches separately. Initial results of experiments conducted using a widely used dataset of US Congress debates BIBREF3 are encouraging and show that this simple model compares well with classification models that employ state-of-the-art distributional text representations (Section SECREF4 ).
Initial results of experiments conducted using a widely used dataset of US Congress debates are encouraging and show that this simple model compares well with classification models that employ state-of-the-art distributional text representations (Section SECREF4 ).
Answer: A widely used dataset of US Congress debates.