A topic-enhanced word embedding for Twitter sentiment classification

Ren, Yafeng; Wang, Ruimin; Ji, Donghong

doi:10.1016/j.ins.2016.06.040

Cited by 127 publications

(47 citation statements)

References 29 publications

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“…In [149] proposed a method based on Twitter sentiment classification using topicenhanced word embedding and also used an LDA model to generate a topic distribution of tweets, considered SVM for classifying tasks in sentiment classification. They used the dataset on SemEval-2014 from Twitter Sentiment Analysis Track.…”

Section: Topic Modeling In Social Network and Microblogsmentioning

confidence: 99%

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

Jelodar

Wang

Yuan

et al. 2018

Multimed Tools Appl

1,100

473

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Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data and text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modelling; Latent Dirichlet Allocation (LDA) is one of the most popular in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper will be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. In addition, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.

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Section: Topic Modeling In Social Network and Microblogsmentioning

confidence: 99%

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

Jelodar

Wang

Yuan

et al. 2018

Multimed Tools Appl

1,100

473

View full text Add to dashboard Cite

show abstract

“…More specifically, word2vec employs neural networks to model the relation between a given word and its context of neighboring words in the given col- This type of model is also referred to as neural language models (Bengio et al, 2003). Recently, word2vec has been shown to be successful in many natural language processing tasks ranging from sentiment analysis (Liang et al, 2015;Ren et al, 2016;Rexha et al, 2016), topic modeling (Bicalho et al, 2017), through document classification (Lilleberg et al, 2015;Yoshikawa et al, 2014) and name entity recognition (Seok et al, 2015;Tang et al, 2014) to machine translation (Freitas et al, 2016;Zou et al, 2013).…”

Section: Continuous-space Modelsmentioning

confidence: 99%

The Influence of Feature Representation of Text on the Performance of Document Classification

2019

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In this paper we perform a comparative analysis of three models for feature representation of text documents in the context of document classification. In particular, we consider the most often used family of models bag-of-words, recently proposed continuous space models word2vec and doc2vec, and the model based on the representation of text documents as language networks. While the bag-of-word models have been extensively used for the document classification task, the performance of the other two models for the same task have not been well understood. This is especially true for the network-based model that have been rarely considered for representation of text documents for classification. In this study, we measure the performance of the document classifiers trained using the method of random forests for features generated the three models and their variants. The results of the empirical comparison show that the commonly used bag-of-words model has performance comparable to the one obtained by the emerging continuous-space model of doc2vec. In particular, the low-dimensional variants of doc2vec generating up to 75 features are among the top-performing document representation models. The results finally point out that doc2vec shows a superior performance in the tasks of classifying large

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“…Chen et al developed the MDK-LDA variant on LDA which takes into account domain knowledge directly to provide better topic descriptors [7]. Furthermore, approaches that combine word embeddings with topic modeling can be beneficial for learning both models jointly [42], as well as improving topic model representations for short texts through word embeddings [36,43,58], or creating improved word embeddings using LDA [46].…”

Section: Semantic Interactionmentioning

confidence: 99%

Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections

El‐Assady

Kehlbeck

Collins

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: Guided relevance feedback for the targeted refinement of incoherent areas in the Semantic Concept Space. This user guidance component tours through the space and highlights potentially uncertain areas, suggesting a recommended action for refinement.Abstract-We present a framework that allows users to incorporate the semantics of their domain knowledge for topic model refinement while remaining model-agnostic. Our approach enables users to (1) understand the semantic space of the model, (2) identify regions of potential conflicts and problems, and (3) readjust the semantic relation of concepts based on their understanding, directly influencing the topic modeling. These tasks are supported by an interactive visual analytics workspace that uses word-embedding projections to define concept regions which can then be refined. The user-refined concepts are independent of a particular document collection and can be transferred to related corpora. All user interactions within the concept space directly affect the semantic relations of the underlying vector space model, which, in turn, change the topic modeling. In addition to direct manipulation, our system guides the users' decisionmaking process through recommended interactions that point out potential improvements. This targeted refinement aims at minimizing the feedback required for an efficient human-in-the-loop process. We confirm the improvements achieved through our approach in two user studies that show topic model quality improvements through our visual knowledge externalization and learning process.

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A topic-enhanced word embedding for Twitter sentiment classification

Cited by 127 publications

References 29 publications

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

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

The Influence of Feature Representation of Text on the Performance of Document Classification

Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections

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