A Novel Method for Extracting Feature Opinion Pairs for Turkish

Türkmen, Hazal; Ekinci, Ekin; Omurca, Sevinç İlhan

doi:10.1007/978-3-319-44748-3_16

Cited by 5 publications

(7 citation statements)

References 13 publications

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“…In this model, frequent itemset mining is used for finding frequently co-occurred pairs. Türkmen et al developed pushdown-automata based aspect-sentiment pair extraction model, which used Turkish linguistic rules, for Turkish user reviews [4]. Amplayo and Hwang implemented Micro Aspect Sentiment Model (MicroASM), which is an LDA model, to generate aspectsentiment pairs for short reviews [18].…”

Section: Related Workmentioning

confidence: 99%

“…And compared with the aspect level analysis both document level and sentence level, for instance, when the hotel reviews are analysed, instead of learning general opinions (good or bad) about the hotel, determining opinions about variety of aspects such as room or main course is more valuable and accurate. Consequently, three main tasks in aspect level sentiment analysis; aspect extraction, sentiment word extraction and aspect-sentiment pair extraction [4].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ekinci

Omurca

2018

ijisae

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“…Everywhere, users are accessing these web sites. Therefore, a growing volume of the data for analysis is piling up and intensively analysed as it exerts a powerful effect on consumers [2].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, there is a need for fine-grained analysis; as a result, aspect-level analysis has gained popularity. Aspect, which expresses the sentiment, is anything that defines and completes a product; sentiment is positive or negative feeling about an aspect [2]. In aspect-level sentiment analysis, sentiments are individually assigned to each aspect.…”

Section: Introductionmentioning

confidence: 99%

Concept-LDA: Incorporating Babelfy into LDA for aspect extraction

Ekinci

Omurca

2019

Journal of Information Science

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Latent Dirichlet allocation (LDA) is one of the probabilistic topic models; it discovers the latent topic structure in a document collection. The basic assumption under LDA is that documents are viewed as a probabilistic mixture of latent topics; a topic has a probability distribution over words and each document is modelled on the basis of a bag-of-words model. The topic models such as LDA are sufficient in learning hidden topics but they do not take into account the deeper semantic knowledge of a document. In this article, we propose a novel method based on topic modelling to determine the latent aspects of online review documents. In the proposed model, which is called Concept-LDA, the feature space of reviews is enriched with the concepts and named entities, which are extracted from Babelfy to obtain topics that contain not only co-occurred words but also semantically related words. The performance in terms of topic coherence and topic quality is reported over 10 publicly available datasets, and it is demonstrated that Concept-LDA achieves better topic representations than an LDA model alone, as measured by topic coherence and F-measure. The learned topic representation by Concept-LDA leads to accurate and an easy aspect extraction task in an aspect-based sentiment analysis system.

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“…However, feature extraction is an important aspect of traditional sentiment analysis (Pang & Lee, 2008;Pannala, et al, 2016;Wilson, Wiebe, & Hoffmann, 2005). As far as feature extraction is concerned, techniques such as unigrams, bigrams, part of speech tags, and combinations of these features are commonly used by linguistics and computer science scholars (e.g., Hollander et al, 2016;Türkmen, Ekinci, & Omurca, 2016).…”

Section: Information Extractionmentioning

confidence: 99%

What Airbnb Reviews can Tell us? An Advanced Latent Aspect Rating Analysis Approach

Luo¹

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There is no doubt that the rapid growth of Airbnb has changed the lodging industry and tourists' behaviors dramatically since the advent of the sharing economy. Airbnb welcomes customers and engages them by creating and providing unique travel experiences to "live like a local" through the delivery of lodging services. With the special experiences that Airbnb customers pursue, more investigation is needed to systematically examine the Airbnb customer lodging experience. Online reviews offer a representative look at individual customers' personal and unique lodging experiences. Moreover, the overall ratings given by customers are reflections of their experiences with a product or service. Since customers take overall ratings into account in their purchase decisions, a study that bridges the customer lodging experience and the overall rating is needed. In contrast to traditional research methods, mining customer reviews has become a useful method to study customers' opinions about products and services. User-generated reviews are a form of evaluation generated by peers that users post on business or other (e.g., third-party) websites (Mudambi & Schuff, 2010). The main purpose of this study is to identify the weights of latent lodging experience aspects that customers consider in order to form their overall ratings based on the eight basic emotions. This study applied both aspect-based sentiment analysis and the latent aspect rating analysis (LARA) model to predict the aspect ratings and determine the latent aspect weights. Specifically, this study extracted the innovative lodging experience aspects that Airbnb customers care about most by mining a total of 248,693 customer reviews from 6,946 Airbnb accommodations. Then, the NRC Emotion Lexicon with eight emotions was employed to assess the sentiments associated with each lodging aspect.

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A Novel Method for Extracting Feature Opinion Pairs for Turkish

Cited by 5 publications

References 13 publications

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

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

Concept-LDA: Incorporating Babelfy into LDA for aspect extraction

What Airbnb Reviews can Tell us? An Advanced Latent Aspect Rating Analysis Approach

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