# TDAM: a Topic-Dependent Attention Model for Sentiment Analysis

## TL;DR

This paper introduces TDAM, a novel topic-dependent attention model that improves sentiment classification and unsupervised topic extraction from user reviews by integrating shared global and local topic embeddings within a hierarchical GRU framework.

## Contribution

The paper presents a new hierarchical architecture with a modified GRU and attention mechanism for joint sentiment analysis and topic extraction without aspect annotations.

## Key findings

- Achieves state-of-the-art sentiment classification performance.
- Outperforms existing methods in topic coherence for unsupervised extraction.
- Effectively extracts coherent aspect-sentiment clusters without aspect-level supervision.

## Abstract

We propose a topic-dependent attention model for sentiment classification and topic extraction. Our model assumes that a global topic embedding is shared across documents and employs an attention mechanism to derive local topic embedding for words and sentences. These are subsequently incorporated in a modified Gated Recurrent Unit (GRU) for sentiment classification and extraction of topics bearing different sentiment polarities. Those topics emerge from the words' local topic embeddings learned by the internal attention of the GRU cells in the context of a multi-task learning framework. In this paper, we present the hierarchical architecture, the new GRU unit and the experiments conducted on users' reviews which demonstrate classification performance on a par with the state-of-the-art methodologies for sentiment classification and topic coherence outperforming the current approaches for supervised topic extraction. In addition, our model is able to extract coherent aspect-sentiment clusters despite using no aspect-level annotations for training.