Text genre classification with genre-revealing and subject-revealing features

Lee, Yong-Bae; Myaeng, Sung Hyon

doi:10.1145/564400.564403

Cited by 29 publications

(32 citation statements)

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“…In addition to classification based on affect and emotion, another related area of research that addresses non-topic-based categorization is that of determining the genre of texts [97,98,150,153,182,277]. Since subjective genres, such as "editorial," are often one of the possible categories, such work can be viewed as closely related to subjectivity detection.…”

Section: Other Non-factual Information In Textmentioning

confidence: 99%

Opinion Mining and Sentiment Analysis

Pang

Lee

2008

FNT in Information Retrieval

5,825

1,889

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An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

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Section: Other Non-factual Information In Textmentioning

confidence: 99%

Opinion Mining and Sentiment Analysis

Pang

Lee

2008

FNT in Information Retrieval

5,825

1,889

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“…Take for example, "In the past 50 years, the New York Times produced 3 billion words" and "Twitter users produce 8 billion words -every single day" [16] [17]. Each of these genres of data, have their own characteristics, that can be harnessed to augment analysis [22]. However, these very large-scale textbased datasets are not very large in terms of file size.…”

Section: Figure 3 Google Trends Searches For Big Data Analytics 2004mentioning

confidence: 99%

Analyzing Trends and Topics in Internet Governance and Cybersecurity Debates Found in Twelve Years of IGF Transcripts

Cogburn

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Internet Governance research generates substantial and innovative, interdisciplinary global scholarship. What are key topics and themes in this research area, and how do they relate to cybersecurity? This paper answers these questions by analyzing transcripts from twelve years of the UN Internet Governance Forum (IGF), asking: (1) What key themes, topics, and entities are discussed at IGF? (2) Which issues have remained consistent at IGF, and which have changed? And (3) to what extent is the NIST Cybersecurity Framework represented at IGF? Using the CRISP-DM approach to text mining, we find human rights as the most dominant IGF theme, followed by freedom of expression, with disability being a persistent issue. During entity extraction cybersecurity emerges prominently, as does blockchain and IoT. Topic Modeling illustrates the resilience of human rights, but also identifies the IANA transition, accessibility, and "fake news." Finally, the NIST cybersecurity framework is represented clearly in the data.

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“…(Brezealed, B. et al, 2006) and (Lin Wei-Hao et al, 2002) propose using a weighting method which is commonly used one for information retrieval, the TF*IDF. (Yong-Bae Lee et al, 2002) introduced the deviation formula of TF*IDF to Tf ratio and Idf ratio to obtain a set of training documents used for the statistic classifier Naive Bayesian. (Brett, K. et al, 1997) describes an approach based on linguistic analysis.…”

Section: Related Workmentioning

confidence: 99%

Automatic Audiovisual Documents Genre Description

Fourati

Jedidi

Gargouri

2014

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

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Audiovisual documents are among the most proliferated resources. Faced with these huge quantities produced every day, the lack of significant descriptions without missing the important content arises. The extraction of these descriptions requires an analysis of the audiovisual document's content. The automation of the process of describing audiovisual documents is essential because of the richness and the diversity of the available analytical criteria. In this paper, we present a method that allows the extraction of a semantic and automatic description from the content such as genre. We chose to describe cinematic audiovisual documents based on the documentation prepared in the pre-production phase of films, namely synopsis. The experimental result on Imdb (Internet Movie Database) and the Wikipedia encyclopedia indicate that our method of genre detection is better than the result of these corpuses.

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Text genre classification with genre-revealing and subject-revealing features

Cited by 29 publications

References 0 publications

Opinion Mining and Sentiment Analysis

Opinion Mining and Sentiment Analysis

Analyzing Trends and Topics in Internet Governance and Cybersecurity Debates Found in Twelve Years of IGF Transcripts

Automatic Audiovisual Documents Genre Description

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