A general framework to expand short text for topic modeling

Bicalho, Paulo Viana; Pita, Marcelo; Pedrosa, Gabriel; Lacerda, Anísio; Pappa, Gisele L.

doi:10.1016/j.ins.2017.02.007

Cited by 68 publications

(32 citation statements)

References 46 publications

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“…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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Section: Continuous-space Modelsmentioning

confidence: 99%

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

2019

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“…Several studies focus on topic extraction from text data that share some characteristics with OE responses, including tweets (Bicalho et al;2017;Hong & Davison, 2010;Jin et al, 2011;Mehrotra et al, 2013;Nguyen et al, 2015;Weng et al, 2010;Yan et al, 2013;Zhao et al, 2011;Zuo et al, 2016), weblogs (Singh, Waila, Piryani, & Uddin, 2013;Tsai, 2011) and online reviews (Brody & Elhadad, 2010;Titov & McDonald, 2008). Due to the lack of established approaches for OE responses, we examine whether approaches for those three types of corpora can be adapted to OE responses.…”

Section: Introductionmentioning

confidence: 99%

“…Several techniques for extracting topics from short texts have been proposed in the literature. A recent study of Bicalho et al (2017) systematizes the field and introduces a general framework for overcoming the specific challenges of short text topic modelling. In general, short text topic models split into two categories: The first one uses auxiliary information to enrich the input (knowledge-based approaches).…”

Section: Introductionmentioning

confidence: 99%

“…In general, short text topic models split into two categories: The first one uses auxiliary information to enrich the input (knowledge-based approaches). Examples include corpus-related metadata (Hong & Davison, 2010;Mehrotra et al, 2013;Weng et al, 2010), external knowledge sources like auxiliary long text (Jin et al, 2011;Phan et al, 2008) or word embeddings (Bicalho et al, 2017;Nguyen et al, 2015). The second category includes corpus-based approaches that rely exclusively on the target corpus, meaning the text corpus from which topics shall be extracted; such as the collection of OE responses in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…Corpus-based approaches modify the topic modelling process itself (Mihalcea, Courtney, & Strapparava, 2006). Examples include the introduction of stronger assumptions about the data (Bicalho et al, 2017;Nguyen et al, 2015;Zhao et al, 2011) or the manipulation of the document generation process (Yan et al, 2013;Zuo et al, 2016). Table 2 outlines relevant prior studies, divided into knowledge-based and corpus-based approaches, including the respective target corpora and methodology.…”

Section: Introductionmentioning

confidence: 99%

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Topic modeling for analyzing open-ended survey responses

Pietsch¹,

Lessmann

2018

Journal of Business Analytics

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Open-ended responses are widely used in market research studies. Processing of such responses requires labour-intensive human coding. This paper focuses on unsupervised topic models and tests their ability to automate the analysis of open-ended responses. Since state-of-the-art topic models struggle with the shortness of open-ended responses, the paper considers three novel short text topic models: Latent Feature Latent Dirichlet Allocation, Biterm Topic Model and Word Network Topic Model. The models are fitted and evaluated on a set of real-world open-ended responses provided by a market research company. Multiple components such as topic coherence and document classification are quantitatively and qualitatively evaluated to appraise whether topic models can replace human coding. The results suggest that topic models are a viable alternative for openended response coding. However, their usefulness is limited when a correct one-to-one mapping of responses and topics or the exact topic distribution is needed.

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