Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey

Jelodar, Hamed; Wang, Yongli; Yuan, Chi; Xia, Feng; Jiang, Xiahui; Li, Yanchao; Zhao, Liang

doi:10.1007/s11042-018-6894-4

Cited by 1,076 publications

(607 citation statements)

References 158 publications

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“…We evaluate the results using Precision ( ), Recall ( ) and F1measure ( ) with Eqs. (12), (13), and (14), respectively.…”

Section: Results and Analysismentioning

confidence: 99%

“…In Natural Language Processing (NLP) Topic Modeling is a very useful method for finding topics and finding semantic relations among many unstructured documents [12]. There are many Topic Modeling methods used by researchers, including Latent Dirichlet Allocation (LDA) [13], Probabilistic Latent Semantic Analysis (PLSA) [14], and LDA2Vec [15].…”

Section: Topic Modellingmentioning

confidence: 99%

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Aspect Based Sentiment Analysis for Restaurant Reviews Using Hybrid ELMoWikipedia and Hybrid Expanded Opinion Lexicon-SentiCircle

Nurifan¹,

Sarno²,

Sungkono³

2019

IJIES

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Many restaurant review analysis have been done, however only few analysis have been done for specific aspects of a restaurant. In this context this paper proposes aspect based restaurant analysis which includes Physical environment, Food quality, Service quality and Price fairness. The analysis steps include Aspect Term Extraction (ATE), Aspect Keyword Extraction (AKE), Aspect Categorization (AC) and Sentiment Analysis (SA). ATE employs the modification of Double Propagation method and several Topic Modelling methods, AKE utilizes Term Frequency-Inverse Cluster Frequency (TF-ICF), in AC we propose Hybrid ELMo-Wikipedia (HEW), and in SA we propose Hybrid Expanded Opinion Lexicon-SentiCircle (HEOLS). The results show that our modification of the methods used in ATE could increase the f1measure of the AC by average 2%, then the HEW that we proposed had better f1measure compared to other similar methods by average 6%. Other than that, our proposed HEOLS can expand and redetermine the Opinion Lexicon polarity and can increase f1measure of SA by 6%.

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“…We evaluate the results using Precision ( ), Recall ( ) and F1measure ( ) with Eqs. (12), (13), and (14), respectively.…”

Section: Results and Analysismentioning

confidence: 99%

Section: Topic Modellingmentioning

confidence: 99%

Aspect Based Sentiment Analysis for Restaurant Reviews Using Hybrid ELMoWikipedia and Hybrid Expanded Opinion Lexicon-SentiCircle

Nurifan¹,

Sarno²,

Sungkono³

2019

IJIES

View full text Add to dashboard Cite

show abstract

“…Other 2 baseline approaches, namely, MedLDA and RTM, which are the extensions of LDA, were presented in the same year [19]. Zhu et al [20] combined the intuition of the max-margin prediction models (such as SVM) with the intuition of hierarchical Bayesian topic models (such as LDA) and called their novel supervised topic model as the maximum entropy discrimination latent Dirichlet allocation (MedLDA) model.…”

Section: Literature Reviewmentioning

confidence: 99%

NET-LDA: a novel topic modeling method based on semantic document similarity

Ekinci¹,

Omurca²

2020

Turk J Elec Eng & Comp Sci

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Topic models, such as latent Dirichlet allocation (LDA), allow us to categorize each document based on the topics. It builds a document as a mixture of topics and a topic is modeled as a probability distribution over words.However, the key drawback of the traditional topic model is that it cannot handle the semantic knowledge hidden in the documents. Therefore, semantically related, coherent and meaningful topics cannot be obtained. However, semantic inference plays a significant role in topic modeling as well as in other text mining tasks. In this paper, in order to tackle this problem, a novel NET-LDA model is proposed. In NET-LDA, semantically similar documents are merged to bring all semantically related words together and the obtained semantic similarity knowledge is incorporated into the model with a new adaptive semantic parameter. The motivation of the study is to reveal the impact of semantic knowledge in the topic model researches. Therefore, in a given corpus, different documents may contain different words but may speak about the same topic. For such documents to be correctly identified, the feature space of the documents must be elaborated with more powerful features. In order to accomplish this goal, the semantic space of documents is constructed with concepts and named entities. Two datasets in the English and Turkish languages and 12 different domains have been evaluated to show the independence of the model from both language and domain. The proposed NET-LDA, compared to the baselines, outperforms in terms of topic coherence, F-measure, and qualitative evaluation.

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“…Topic modeling is a powerful algorithm for discovering hidden structures in large text sets. It is widely used in natural language processing, text mining, social media analysis, information retrieval, and other fields [47]. LDA was proposed by Pritchard, Stephens, and Donnelly [48] and is a widely applied approach in topic modeling.…”

Section: Topic Modelling Analysis Of Online Reviewsmentioning

confidence: 99%

Museum Tourism 2.0: Experiences and Satisfaction with Shopping at the National Gallery in London

et al. 2019

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The tourist shopping experience is the sum of the satisfaction or dissatisfaction from the individual attributes of purchased products and services. With the popularity of the Internet and travel review websites, more people choose to upload their tour experiences on their favorite social media platforms, which can influence another's travel planning and choices. However, there have been few investigations of social media reviews of tourist shopping experiences and especially of satisfaction with museum tourism shopping. This research analyzed the user-generated reviews of the National Gallery (NG) in London written in the English language on TripAdvisor to learn more about tourist shopping experience in museums. The Latent Dirichlet Allocation (LDA) topic model was used to discover the underlying themes of online reviews and keywords related to these shopping experiences. Sentiment analysis based on a purpose-developed dictionary was conducted to explore the dissatisfying aspects of tourist shopping experiences. The results provide a framework for museums to improve shopping experiences and enhance their future development.Sustainability 2019, 11, 7108 2 of 16 cultural experiences, visitors can attend learning programs organized by museums and interact with instructors [9]. Sociability refers to the experience that both visitors and members seek. Museums hold events to meet social needs. For example, museums can have more seating, social spaces for members, dining facilities, and even grandparents' rooms. In addition, the recreational experience is an important element of the museum [5,9].Moreover, with the rapid growth of digitalization and informatization as well as the propagation of consumer commentaries, many museums visitors write reviews and post them on social media. Consumers create authentic content and share their positive and/or negative emotional experiences on social media [10]. These trends make it possible to collect large amounts of data from various social networks to evaluate corporate performance, improve customer experience, and identify opportunities for service innovation from the customer's perspective [10][11][12].This research analyzed online reviews from TripAdvisor (www.tripadvisor.com), the largest social travel website in the world, with about 315 million reviewers and over 730 million reviews of hotels, restaurants, attractions, airlines, and other travel-related businesses [13] under the four-dimension experience framework proposed by Kotler [9].The following research questions were addressed:

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Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey

Cited by 1,076 publications

References 158 publications

Aspect Based Sentiment Analysis for Restaurant Reviews Using Hybrid ELMoWikipedia and Hybrid Expanded Opinion Lexicon-SentiCircle

Aspect Based Sentiment Analysis for Restaurant Reviews Using Hybrid ELMoWikipedia and Hybrid Expanded Opinion Lexicon-SentiCircle

NET-LDA: a novel topic modeling method based on semantic document similarity

Museum Tourism 2.0: Experiences and Satisfaction with Shopping at the National Gallery in London

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