Topics modeling based on selective Zipf distribution

Zeng, Jianping; Duan, Jing; Cao, Wei; Wu, Chengrong

doi:10.1016/j.eswa.2011.12.051

Cited by 24 publications

(14 citation statements)

References 8 publications

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“…(1) and (2).Figure 4: Speeches' length distribution in term of total number of words used per speech over the yearsthe bias onα. Further evidence of the impressive capacity of such a fitting can be noticed with a visual inspection ofFigures 11,12,14,15,19,20. Therefore, jointly with the Shapiro-Wilk test p-values reported in…”

mentioning

confidence: 88%

A joint text mining-rank size investigation of the rhetoric structures of the US Presidents’ speeches

Ficcadenti

Cerqueti

Ausloos

2019

Expert Systems with Applications

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This work presents a text mining context and its use for a deep analysis of the messages delivered by the politicians. Specifically, we deal with an expert systems-based exploration of the rhetoric dynamics of a large collection of US Presidents' speeches, ranging from Washington to Trump. In particular, speeches are viewed as complex expert systems whose structures can be effectively analyzed through rank-size laws. The methodological contribution of the paper is twofold. First, we develop a text mining-based procedure for the construction of the dataset by using a web scraping routine on the Miller Center website -the repository collecting the speeches. Second, we explore the implicit structure of the discourse data by implementing a rank-size procedure over the individual speeches, being the words of each speech ranked in terms of their frequencies. The scientific significance of the proposed combination of text-mining and rank-size approaches can be found in its flexibility and generality, which let it be reproducible to a wide set of expert systems and text mining contexts. The usefulness of the proposed method and the speech subsequent analysis is demonstrated by the findings themselves. Indeed, in terms of impact, it is worth noting that interesting conclusions of social, political and linguistic nature on how 45 United States Presidents, from April 30, 1789 till February 28, 2017 delivered political messages can be carried out. Indeed, the proposed analysis shows some remarkable regularities, not only inside a given speech, but also among different speeches. Moreover, under a purely methodological perspective, the presented contribution suggests possible ways of generating a linguistic decision-making algorithm.

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confidence: 88%

A joint text mining-rank size investigation of the rhetoric structures of the US Presidents’ speeches

Ficcadenti

Cerqueti

Ausloos

2019

Expert Systems with Applications

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“…Further, low-informative terms are identified by partof-speech tagging where the grammatical role of a term is identified. A large number of terms is reduced by considering terms that occur more frequently than once or twice also known as Zipf's law ( Zeng, Duan, Cao, & Wu, 2012;Zipf, 1949 ). Last, related terms with the same stem can be grouped together by stemming.…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

Identification of interdisciplinary ideas

Thorleuchter

Poel

2016

Information Processing & Management

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“…Topic Modeling is the main area of this activity. For Zeng et al [22], topic is considered an aggregate of words and their frequency, which can be extracted from a document and is an important unit of the Topic Modeling Process.…”

Section: Introductionmentioning

confidence: 99%

Topic Modeling based on Louvain method in Online Social Networks

Kido¹,

Igawa²,

Barbon³

2016

Anais Do Simpósio Brasileiro De Sistemas De Informação (SBSI)

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Online Social Networks (OSNs) are the most used media nowadays, such as Twitter. The OSNs provide valuable information to marketing and competitiveness based on users posts and opinions stored inside huge volume of data from several themes, topics and subjects. In order to mining the topics discussed on an OSN we present a novel application of Louvain method for Topic Modeling based on communities detection in graphs by modularity. The proposed approach succeeded in finding topics in five different datasets composed of textual content from Twitter and Youtube. Another important contribution achieved was about the presence of texts posted by spammers. In this case, a particular behavior observed by graph architecture (density and degree) allows the classification of a topic as natural or artificial, this last created by the spammers on OSNs.

show abstract

Topics modeling based on selective Zipf distribution

Cited by 24 publications

References 8 publications

A joint text mining-rank size investigation of the rhetoric structures of the US Presidents’ speeches

A joint text mining-rank size investigation of the rhetoric structures of the US Presidents’ speeches

Identification of interdisciplinary ideas

Topic Modeling based on Louvain method in Online Social Networks

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