Abstract
With the development of sentiment analysis, studies have been gradually classified based on different researched candidates. Among them, aspect-based sentiment analysis plays an important role in subtle opinion mining for online reviews. It used to be treated as a group of pipeline tasks but has been proved to be analysed well in an end-to-end model recently. Due to less labelled resources, the need for cross-domain aspect-based sentiment analysis has started to get attention. However, challenges exist when seeking domain-invariant features and keeping domain-dependent features to achieve domain adaptation within a fine-grained task. This paper utilizes the domain-dependent embeddings and designs the model CD-E2EABSA to achieve cross-domain aspect-based sentiment analysis in an end-to-end fashion. The proposed model utilizes the domain-dependent embeddings with a multitask learning strategy to capture both domain-invariant and domain-dependent knowledge. Various experiments are conducted and show the effectiveness of all components on two public datasets. Also, it is also proved that as a cross-domain model, CD-E2EABSA can perform better than most of the in-domain ABSA methods.
Highlights
Sentiment analysis, as one of the most popular tools in the natural language processing (NLP) domain, has attracted enormous explorations [1]. rough the process of sentiment analysis, which is named as opinion mining, the sentiment tendency of texts is extracted
Aspectbased sentiment analysis (ABSA) was used to be treated as a task that composed of several steps or subtasks, including aspect extraction that consists of opinion target extraction
For the fine-grained sentiment analysis problem that is regarded as a sequence labelling task, this paper adopts a more advanced word vector generation layer and transforms the traditional BiLSTM-Conditional Random Field (CRF) network by introducing domain vectors to achieve cross-domain aspectbased sentiment analysis. is section will illustrate the structure of the model, which is named as CD-E2EABSA and shown in Figure 3. e CD-E2EABSA mainly consists of a word embedding layer, shared-ABSA layer with a parameter generation network, and private CRF layer for each domain
Summary
As one of the most popular tools in the natural language processing (NLP) domain, has attracted enormous explorations [1]. rough the process of sentiment analysis, which is named as opinion mining, the sentiment tendency of texts is extracted. Rough the process of sentiment analysis, which is named as opinion mining, the sentiment tendency of texts is extracted. Based on different research objects, the field of sentiment analysis can be classified into three levels, including document level, sentence level, and aspect level. When considering the practical opinion mining in user feedback related to products or services, one or more entities, the evaluation of which might be different, are mentioned in one sentence, which demands a more finegrained analysis method. Is paper will focus on the aspect-based sentiment analysis and extracting the author’s feelings about certain entities or targets [3]. Aspectbased sentiment analysis (ABSA) was used to be treated as a task that composed of several steps or subtasks, including aspect extraction that consists of opinion target extraction
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