# [論文速速讀]Attention-based LSTM for Aspect-level Sentiment Classification

Posted by John on 2020-05-25
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## Abstract

Aspect-level sentiment classification is a fine-grained task in sentiment analysis.

In this paper, we reveal that the sentiment polarity of a sentence is not only determined by the content but is also highly related to the concerned aspect. For instance, “The appetizers are ok, but the service is slow.”, for aspect taste, the polarity is positive while for service, the polarity is negative.

Attentionすごい

## Introduction

The main contributions of our work can be summarized as follows:

• We propose attention-based Long Short-Term memory for aspect-level sentiment classification. The models are able to attend different parts of a sentence when different aspects are concerned. Results show that the attention mechanism is effective.
• Since aspect plays a key role in this task, we propose two ways to take into account aspect information during attention: one way is to concatenate the aspect vector into the sentence hidden representations for computing attention weights, and another way is to additionally append the aspect vector into the input word vectors.
• Experimental results indicate that our approach can improve the performance compared with several baselines, and further examples
demonstrate the attention mechanism works well for aspect-level sentiment classification.

Jumping

## Attention-based LSTM with Aspect Embedding

### LSTM with Aspect Embedding (AE-LSTM)

Aspect information is vital when classifying the polarity of one sentence given aspect. We may get opposite polarities if different aspects are considered.
To make the best use of aspect information, we propose to learn an embedding vector for each aspect.

$v_{\alpha_i}\in \mathbb{R}^{d_\alpha}$是aspect $i$的embedding，$d_{\alpha}$是dimension

$A\in \mathbb{R}^{d_{\alpha}\times|A|}$是所有的embeeding matrix

### Attention-based LSTM (AT-LSTM)

• $H \in \mathbb{R}^{d \times N}$, $[h_{1}, \cdots, h_{N}]$ 是LSTM的hidden vector
• $v_{a} \in \mathbb{R}^{d_{a}}$ 是某個aspect的embedding

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