Aspect Extraction Performance with POS Tag Pattern of Dependency Relation in Aspect-based Sentiment Analysis

Abstract

The most important task in aspect-based sentiment analysis (ABSA) is the aspect and sentiment word extraction. It is a challenge to identify and extract each aspect and it specific associated sentiment word correctly in the review sentence that consists of multiple aspects with various polarities expressed for multiple sentiments. By exploiting the dependency relation between words in a review, the multiple aspects and its corresponding sentiment can be identified. However, not all types of dependency relation patterns are able to extract candidate aspect and sentiment word pairs. In this paper, a preliminary study was performed on the performance of different type of dependency relation with different POS tag patterns in pre-extracting candidate aspect from customer review. The result contributes to the identification of the specific type dependency relation with it POS tag pattern that lead to high aspect extraction performance. The combination of these dependency relations offers a solution for single aspect single sentiment and multi aspect multi sentiment cases.

A Survey on Opinion Mining: From Stance to Product Aspect

“…Besides, it is a challenge work to extract aspect and opinion correctly in the review that consists of multiple aspect with various opinion. Shafie [59] employs a dependency parser to capture the relation between aspect and opinion words and solves the problem.…”

Aspect Category Extraction for Sentiment Analysis using Multivariate Filter Method of Feature Selection

“…Then a test set is utilized to verify the model by deriving the class labels of unknown features. Some feature components that are used for feature categorization are unigrams, bigrams NLP based [1], [7] and ontology-based features [13]. Now days, many systems use word dependency-based and ontology-based features [16] to train the classifier.…”

“…In this approach, the weight of each term is calculated using equation (1). The weight of a term is the conditional probability where is the occurrence count of term in aspect category and is the total occurrence count of a term in all aspect categories.…”

“…Section II is related work, the proposed system is described in section III, section IV shows the results of experimentation and section V contains the conclusion followed by the future scope. [1], [3], [8], etc. Product review based sentiment analysis is similar to the proposed sentiment analysis approach.…”

Aspect Level Sentiment Analysis Methods Applied to Text in Formal Military Reports

“…Sentiment analysis -analysing people's opinions, sentiments, appraisals, attitudes, and emotions toward specific entities such as services, objects or individuals. 1 These aspects are often expressed in textual documents and are used by analysts to help identify the most relevant information from documents. Sentiment analysis can be done on different levels of document granularity; overall sentiment across the whole document, over a paragraph or a sentence.…”