Abstract
This paper is focused on aspect term extraction in aspect-based sentiment analysis (ABSA), which is one of the hot spots in natural language processing (NLP). This paper proposes MFE-CRF that introduces Multi-Feature Embedding (MFE) clustering based on the Conditional Random Field (CRF) model to improve the effect of aspect term extraction in ABSA. First, Multi-Feature Embedding (MFE) is proposed to improve the text representation and capture more semantic information from text. Then the authors use kmeans++ algorithm to obtain MFE and word clustering to enrich the position features of CRF. Finally, the clustering classes of MFE and word embedding are set as the additional position features to train the model of CRF for aspect term extraction. The experiments on SemEval datasets validate the effectiveness of this model. The results of different models indicate that MFE-CRF can greatly improve the Recall rate of CRF model. Additionally, the Precision rate also is increased obviously when the semantics of text is complex.
Highlights
Sentiment analysis is one of the hot spots of natural language processing (NLP), which aims to mine opinions, sentiments, and emotions based on observations of people’s actions that can be captured using their writing [1]
The purpose of this paper is to study aspect term extraction of aspect-based sentiment analysis (ABSA)
Multi-Feature Embedding (MFE) clustering reinforcement is introduced on the basis of Conditional Random Field (CRF) using the general position features
Summary
Sentiment analysis is one of the hot spots of natural language processing (NLP), which aims to mine opinions, sentiments, and emotions based on observations of people’s actions that can be captured using their writing [1]. The early sentiment analysis focuses on the coarse-grained emotion analysis at the document-level and sentence-level, such as the sentiment polarity of documents, or the subjectivity and opinion mining of sentences. The coarse-grained emotion analysis tries to detect the overall polarity (negative or positive) of a sentence (or a document) regardless of the target entities and their aspects [2]. The document-level sentiment analysis only makes a judgment on the whole document, but ABSA requires identifying the entities that are endowed with reviews, such as food taste and service quality. ABSA has a great reference value to create a recommendation system for e-commerce services and commodities classification [5]
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