Incorporating Language Patterns and Domain Knowledge into Feature-Opinion Extraction

Zhou, Erqiang; Luo, Xi; Qin, Zhiguang

doi:10.1007/978-3-319-10816-2_26

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Later on, the opinion words are used to calculate the polarity of features in product reviews. Zhou et al (2014) studied feature-opinion extraction from online reviews. The features and opinions in the reviews are extracted using rule-based.…”

Section: Rule-basedmentioning

confidence: 99%

Opinion Extraction on Online Malay Text

Selvaraju¹,

Anbananthen²

2019

American Journal of Applied Sciences

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Growing of social media usage present a new set of opportunities and challenges in the way of information is retrieved and searched. Opinions on social media has become an important factor in influencing people choices on purchasing a product and service. Hence, sentiment analysis has become the most crucial tool in tracking people feedbacks on products and services. For Malay language there is limited sources available for this language. Thus, in this paper we present the method of extracting opinion on online Malay text. The traditional method using POS extraction is not adequate. Thus, rule based method is integrated with POS extraction method to improve opinion words extraction. Most of the existing tools are able to retrieve opinion at sentence and document level. More detail analysis is acquired to have detail information and summarization of a product. This is where feature level sentiment analysis is needed. The process of identifying opinion of a particular feature in a sentence, can be quite tedious and troublesome. This is because opinion of the feature can be hidden and scattered in the sentence. Therefore, opinion mapping is employed for opinion extraction at feature level in this paper. A set of tweets from telecommunication domain is used to evaluate the proposed framework. From the experiment, the accuracy of the extraction performed is 88%. The detail description of the feature level opinion extraction steps is discussed in this paper.

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Section: Rule-basedmentioning

confidence: 99%

Opinion Extraction on Online Malay Text

Selvaraju¹,

Anbananthen²

2019

American Journal of Applied Sciences

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“…Klinger and Cimiano handled aspect-sentiment pair extraction as a joint inference problem and they used imperatively defined factor graphs for extracting pairs [14]. Zhou et al incorporated domain independent language patterns and domain knowledge as a lexical base for aspect-sentiment pair extraction from Chinese restaurant reviews [15]. Quan and Ren extracted aspect-sentiment pairs based on dependency distance between aspects and sentiment calculated with dependency parser [16].…”

Section: Related Workmentioning

confidence: 99%

An Aspect-Sentiment Pair Extraction Approach Based on Latent Dirichlet Allocation

Ekinci

Omurca

2018

ijisae

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Online user reviews have a great influence on decision-making process of customers and product sales of companies. However, it is very difficult to obtain user sentiments among huge volume of data on the web consequently; sentiment analysis has gained great importance in terms of analyzing data automatically. On the other hand, sentiment analysis divides itself into branches and can be performed better with aspect level analysis. In this paper, we proposed to extract aspect-sentiment pairs from a Turkish reviews dataset. The proposed task is the fundamental and indeed the critical step of the aspect level sentiment analysis. While extracting aspect-sentiment pairs, an unsupervised topic model Latent Dirichlet Allocation (LDA) is used. With LDA, aspect-sentiment pairs from user reviews are extracted with 0.86 average precision based on ranked list. The aspect-sentiment pair extraction problem is first time realized with LDA on a realworld Turkish user reviews dataset. The experimental results show that LDA is effective and robust in aspect-sentiment pair extraction from user reviews.

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