Cross‐validation of neural network applications for automatic new topic identification

Özmutlu, H. Cenk; Çavdur, Fatih; Ozmutlu, Seda

doi:10.1002/asi.20696

Cited by 12 publications

(10 citation statements)

References 61 publications

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“…Meta‐tagging tools, like Open Calais, are improving and, when combined with approaches like those outlined in earlier studies (Beitzel et al, 2007; Özmutlu, Çavdur, Özmutlu, 2008; Shen et al, 2006), one can use such tools for making measureable improvements in topical query classification. Accuracy rates for classification of Web queries are reportedly between 40 and 70 percent, which is high given the unbelievable range, structure, semantics, and syntax of Web queries.…”

Section: Resultsmentioning

confidence: 99%

To what degree can log data profile a web searcher?

Jansen

Zhang

Booth

et al. 2009

Proc of Assoc for Info

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In this paper, we report ongoing efforts in a large scale research project to develop methods for profiling individual Web search engine users by leveraging data recorded in the transaction logs of search engines. Our research aim is to investigate how completely one can profile a Web searcher using log data. Taking a broad brush approach, we present an array of profiling attributes to illustrate the spectrum of user characteristics possible from log data. Specifically, we present ongoing research for determining a user 's location, geographical interest, topic of interest, level of interest, the degree of commercial intent, whether the user plans to make a purchase, and whether the user will click a link. We present the state of our ongoing research in user profiling along with that of other researchers. Our findings show that one can develop a fairly robust profile of a Web searcher using log data. We also discuss issues of determining the specific identity of the user. We conclude with a discussion of the implications for the areas of system development, online advertising, privacy, and policies concerning the use of such profiling.

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Section: Resultsmentioning

confidence: 99%

To what degree can log data profile a web searcher?

Jansen

Zhang

Booth

et al. 2009

Proc of Assoc for Info

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“…Their experiments show that 76% of session changes and 92% of session continuations are identified correctly. By extending this work, Özmutlu, Çavdur, and Özmutlu () then report that it is possible for neural networks to perform effective session identification even if the training data and test data are from different search engine data logs.…”

Section: Related Workmentioning

confidence: 85%

Session analysis of people search within a professional social network

Wang

Tian

et al. 2013

J Am Soc Inf Sci Tec

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We perform session analysis for our domain of people search within a professional social network. We find that the content-based method is appropriate to serve as a basis for the session identification in our domain. However, there remain some problems reported in previous research which degrade the identification performance (such as accuracy) of the content-based method. Therefore, in this article, we propose two important refinements to address these problems. We describe the underlying rationale of our refinements and then empirically show that the content-based method equipped with our refinements is able to achieve an excellent identification performance in our domain (such as 99.820% accuracy and 99.707% F-measure in our experiments). Next, because the time-based method has extremely low computation costs, which makes it suitable for many realworld applications, we investigate the feasibility of the time-based method in our domain by evaluating its identification performance based on our refined content-based method. Our experiments demonstrate that the performance of the time-based method is potentially acceptable to many real applications in our domain. Finally, we analyze several features of the identified sessions in our domain and compare them with the corresponding ones in general web search. The results illustrate the professionoriented characteristics of our domain.

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“…To give an example, the tweets sharing the vision of a company or discussing it in a positive way were classified as tweets of the symbolic area; on the other hand, tweets dealing with the creativity of a company have been classified with two labels (political and behavioral). Many algorithms are available for text classification, such as Naive Bayes (Lewis, ), Nearest Neighbor (Yang, ), Neural Networks (Ozmutlu, Cavdur, & Ozmutlu, ), Rule Induction (Apte, Damerau, & Weiss, ), and Support Vector Machines (SVM; Vapnik, ). SVM were included among the most effective classification techniques, with the additional advantage of being more generalizable than others, such as decision trees (Lee & Lee, ; Wang, Sun, Zhang, & Li, ).…”

Section: Methodsmentioning

confidence: 99%

Assessing perceived organizational leadership styles through twitter text mining

Bella¹,

Colladon²,

Battistoni³

et al. 2017

Asso for Info Science & Tech

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We propose a text classification tool based on support vector machines for the assessment of organizational leadership styles, as appearing to Twitter users. We collected Twitter data over 51 days, related to the first 30 Italian organizations in the 2015 ranking of Forbes Global 2000—out of which we selected the five with the most relevant volumes of tweets. We analyzed the communication of the company leaders, together with the dialogue among the stakeholders of each company, to understand the association with perceived leadership styles and dimensions. To assess leadership profiles, we referred to the 10‐factor model developed by Barchiesi and La Bella in 2007. We maintain the distinctiveness of the approach we propose, as it allows a rapid assessment of the perceived leadership capabilities of an enterprise, as they emerge from its social media interactions. It can also be used to show how companies respond and manage their communication when specific events take place, and to assess their stakeholder's reactions.

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Cross‐validation of neural network applications for automatic new topic identification

Cited by 12 publications

References 61 publications

To what degree can log data profile a web searcher?

To what degree can log data profile a web searcher?

Session analysis of people search within a professional social network

Assessing perceived organizational leadership styles through twitter text mining

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