Tapping the power of text mining

Fan, Weiguo; Wallace, Linda G.; Rich, Stephanie; Zhang, Zhongju

doi:10.1145/1151030.1151032

Cited by 323 publications

(179 citation statements)

References 5 publications

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“…Rooted in information retrieval, text mining (or text analytics) is a set of techniques used to discover new knowledge by automatically extract information from free-text documents, which include extraction of features from single documents and the analysis of the feature distribution over the collection of documents to detect interesting patterns and trends (Dörre, Gerstl, and Seiffert 1999). The advancement in NLP technologies allows for these processes in text mining: information extraction (i.e., identifying key phrases within text), topic tracking, summarization (i.e., reducing document length while retaining its main points), categorization (i.e., identifying main themes or "bag of words"), clustering, concept linkage (i.e., connecting documents with shared concepts), information visualization, and question answering (Fan, Wallace, Rich, and Zhang 2006). For tourism and hospitality businesses, text mining techniques can be valuable in handling voluminous online review data to extract important features (e.g., accommodation attributes) and detect patterns and trends to better understand their consumers and competition.…”

Section: Analytics Of Online Reviewsmentioning

confidence: 99%

Hotels vs. Peer-to-Peer Accommodation Rentals: Text Analytics of Consumer Reviews in Portland, Oregon

Tussyadiah

Zach

2015

SSRN Journal

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Section: Analytics Of Online Reviewsmentioning

confidence: 99%

Hotels vs. Peer-to-Peer Accommodation Rentals: Text Analytics of Consumer Reviews in Portland, Oregon

Tussyadiah

Zach

2015

SSRN Journal

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“…contexts, such as to classify news stories, detect fraud, and improve customer support (e.g., Han et al 2002;Fan et al 2006;Cecchini et al 2007). In the information security context, we apply text mining techniques to the contents of disclosed security risk factors so as to identify and categorize the elements of security risk factors that might associate with future incident announcements.…”

Section: Feldman and Sanger 2006) For Example Text Mining Techniquementioning

confidence: 99%

The Association Between the Disclosure and the Realization of Information Security Risk Factors

Wang

Kannan

Ulmer

2013

Information Systems Research

144

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Firms often disclose information security risk factors in public filings such as 10-K reports.The internal information associated with disclosures may be positive or negative. In this paper, we are interested in evaluating how the nature of security risk factors disclosed, which is believed to represent the internal information regarding information security, is associated with future breach announcements. For this purpose, we build a decision tree model, which classifies the occurrence of future security breaches based on the textual contents of the disclosed security risk factors. The model is able to accurately associate disclosure characteristics with breach announcements about 77% of the time. We further explore the contents of the security risk factors using text mining techniques to provide a richer interpretation of the results. The results show that the security risk factors with action-oriented terms and phrases are less likely to be related to future incidents. We also conduct a cross-sectional analysis to study how the market interprets the nature of information security risk factors in annual reports at different time points.We find that the market reaction following the security breach announcement is different depending on the nature of disclosure. Thus, our paper contributes to the literature in information security and sheds light on how market participants can better interpret security risk factors disclosed in financial reports at the time when financial reports are released.

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“…A good summary should be indicative as well as informative [2]. Indicative summaries points out some important parts while an informative emphasizes on the important information in a document.…”

Section: Introductionmentioning

confidence: 99%

Trends in Extractive and Abstractive Techniques in Text Summarization

Bhatia¹,

Jaiswal²

2015

IJCA

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Text Summarization was proved to be an advantage over manually summarizing the large data. It condenses the salient features from the text by preserving the content and serves the meaningful summary. Classification can be done in two ways -extractive and abstractive summarization. Extractive summarization uses statistical and linguistic features to determine the important features and fuse them into a shorter version. Whereas abstractive summarization understands the whole document and then generates the summary. In this paper extractive and abstractive methods are framed.

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Tapping the power of text mining

Abstract: Sifting through vast collections of unstructured or semistructured data beyond the reach of data mining tools, text mining tracks information sources, links isolated concepts in distant documents, maps relationships between activities, and helps answer questions.

Cited by 323 publications

References 5 publications

Hotels vs. Peer-to-Peer Accommodation Rentals: Text Analytics of Consumer Reviews in Portland, Oregon

Hotels vs. Peer-to-Peer Accommodation Rentals: Text Analytics of Consumer Reviews in Portland, Oregon

The Association Between the Disclosure and the Realization of Information Security Risk Factors

Trends in Extractive and Abstractive Techniques in Text Summarization

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