# Implicit Dimension Identification in User-Generated Text with LSTM Networks

## TL;DR

This paper introduces new datasets and compares classification methods to detect implicit beliefs and perspectives in user-generated text, focusing on political bias and question types.

## Contribution

It presents novel annotated datasets for implicit belief detection and evaluates various classifiers on political and conversational text analysis tasks.

## Key findings

- New datasets for implicit belief and perspective detection
- Classification performance differences across tasks
- Narrative language analysis for political perspective

## Abstract

In the process of online storytelling, individual users create and consume highly diverse content that contains a great deal of implicit beliefs and not plainly expressed narrative. It is hard to manually detect these implicit beliefs, intentions and moral foundations of the writers. We study and investigate two different tasks, each of which reflect the difficulty of detecting an implicit user's knowledge, intent or belief that may be based on writer's moral foundation: 1) political perspective detection in news articles 2) identification of informational vs. conversational questions in community question answering (CQA) archives and. In both tasks we first describe new interesting annotated datasets and make the datasets publicly available. Second, we compare various classification algorithms, and show the differences in their performance on both tasks. Third, in political perspective detection task we utilize a narrative representation language of local press to identify perspective differences between presumably neutral American and British press.