Search (14 results, page 1 of 1)

Miao, Q.; Li, Q.; Zeng, D.: Fine-grained opinion mining by integrating multiple review sources (2010) 0.00

0.0028607734 = product of:
  0.028607734 = sum of:
    0.028607734 = product of:
      0.0858232 = sum of:
        0.0858232 = weight(_text_:2010 in 4104) [ClassicSimilarity], result of:
          0.0858232 = score(doc=4104,freq=5.0), product of:
            0.14672957 = queryWeight, product of:
              4.7831497 = idf(docFreq=1005, maxDocs=44218)
              0.03067635 = queryNorm
            0.5849073 = fieldWeight in 4104, product of:
              2.236068 = tf(freq=5.0), with freq of:
                5.0 = termFreq=5.0
              4.7831497 = idf(docFreq=1005, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4104)
      0.33333334 = coord(1/3)
  0.1 = coord(1/10)

Source: Journal of the American Society for Information Science and Technology. 61(2010) no.11, S.2288-2299
Year: 2010

Thelwall, M.; Wilkinson, D.: Public dialogs in social network sites : What is their purpose? (2010) 0.00

0.0024520915 = product of:
  0.024520915 = sum of:
    0.024520915 = product of:
      0.07356274 = sum of:
        0.07356274 = weight(_text_:2010 in 3327) [ClassicSimilarity], result of:
          0.07356274 = score(doc=3327,freq=5.0), product of:
            0.14672957 = queryWeight, product of:
              4.7831497 = idf(docFreq=1005, maxDocs=44218)
              0.03067635 = queryNorm
            0.5013491 = fieldWeight in 3327, product of:
              2.236068 = tf(freq=5.0), with freq of:
                5.0 = termFreq=5.0
              4.7831497 = idf(docFreq=1005, maxDocs=44218)
              0.046875 = fieldNorm(doc=3327)
      0.33333334 = coord(1/3)
  0.1 = coord(1/10)

Source: Journal of the American Society for Information Science and Technology. 61(2010) no.2, S.392-404
Year: 2010

Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010) 0.00

0.0024520915 = product of:
  0.024520915 = sum of:
    0.024520915 = product of:
      0.07356274 = sum of:
        0.07356274 = weight(_text_:2010 in 3464) [ClassicSimilarity], result of:
          0.07356274 = score(doc=3464,freq=5.0), product of:
            0.14672957 = queryWeight, product of:
              4.7831497 = idf(docFreq=1005, maxDocs=44218)
              0.03067635 = queryNorm
            0.5013491 = fieldWeight in 3464, product of:
              2.236068 = tf(freq=5.0), with freq of:
                5.0 = termFreq=5.0
              4.7831497 = idf(docFreq=1005, maxDocs=44218)
              0.046875 = fieldNorm(doc=3464)
      0.33333334 = coord(1/3)
  0.1 = coord(1/10)

Source: Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1105-1119
Year: 2010

Leydesdorff, L.; Persson, O.: Mapping the geography of science : distribution patterns and networks of relations among cities and institutes (2010) 0.00

0.0024520915 = product of:
  0.024520915 = sum of:
    0.024520915 = product of:
      0.07356274 = sum of:
        0.07356274 = weight(_text_:2010 in 3704) [ClassicSimilarity], result of:
          0.07356274 = score(doc=3704,freq=5.0), product of:
            0.14672957 = queryWeight, product of:
              4.7831497 = idf(docFreq=1005, maxDocs=44218)
              0.03067635 = queryNorm
            0.5013491 = fieldWeight in 3704, product of:
              2.236068 = tf(freq=5.0), with freq of:
                5.0 = termFreq=5.0
              4.7831497 = idf(docFreq=1005, maxDocs=44218)
              0.046875 = fieldNorm(doc=3704)
      0.33333334 = coord(1/3)
  0.1 = coord(1/10)

Source: Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1622-1634
Year: 2010

Berendt, B.; Krause, B.; Kolbe-Nusser, S.: Intelligent scientific authoring tools : interactive data mining for constructive uses of citation networks (2010) 0.00

0.0024520915 = product of:
  0.024520915 = sum of:
    0.024520915 = product of:
      0.07356274 = sum of:
        0.07356274 = weight(_text_:2010 in 4226) [ClassicSimilarity], result of:
          0.07356274 = score(doc=4226,freq=5.0), product of:
            0.14672957 = queryWeight, product of:
              4.7831497 = idf(docFreq=1005, maxDocs=44218)
              0.03067635 = queryNorm
            0.5013491 = fieldWeight in 4226, product of:
              2.236068 = tf(freq=5.0), with freq of:
                5.0 = termFreq=5.0
              4.7831497 = idf(docFreq=1005, maxDocs=44218)
              0.046875 = fieldNorm(doc=4226)
      0.33333334 = coord(1/3)
  0.1 = coord(1/10)

Source: Information processing and management. 46(2010) no.1, S.1-10
Year: 2010

Huvila, I.: Mining qualitative data on human information behaviour from the Web (2010) 0.00

0.0022159456 = product of:
  0.022159455 = sum of:
    0.022159455 = product of:
      0.066478364 = sum of:
        0.066478364 = weight(_text_:2010 in 4676) [ClassicSimilarity], result of:
          0.066478364 = score(doc=4676,freq=3.0), product of:
            0.14672957 = queryWeight, product of:
              4.7831497 = idf(docFreq=1005, maxDocs=44218)
              0.03067635 = queryNorm
            0.45306724 = fieldWeight in 4676, product of:
              1.7320508 = tf(freq=3.0), with freq of:
                3.0 = termFreq=3.0
              4.7831497 = idf(docFreq=1005, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4676)
      0.33333334 = coord(1/3)
  0.1 = coord(1/10)

Year: 2010

Kong, S.; Ye, F.; Feng, L.; Zhao, Z.: Towards the prediction problems of bursting hashtags on Twitter (2015) 0.00
```
0.0014247395 = product of:
  0.014247394 = sum of:
    0.014247394 = product of:
      0.04274218 = sum of:
        0.04274218 = weight(_text_:problem in 2338) [ClassicSimilarity], result of:
          0.04274218 = score(doc=2338,freq=2.0), product of:
            0.1302053 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.03067635 = queryNorm
            0.3282676 = fieldWeight in 2338, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0546875 = fieldNorm(doc=2338)
      0.33333334 = coord(1/3)
  0.1 = coord(1/10)
```
Abstract

Hundreds of thousands of hashtags are generated every day on Twitter. Only a few will burst and become trending topics. In this article, we provide the definition of a bursting hashtag and conduct a systematic study of a series of challenging prediction problems that span the entire life cycles of bursting hashtags. Around the problem of "how to build a system to predict bursting hashtags," we explore different types of features and present machine learning solutions. On real data sets from Twitter, experiments are conducted to evaluate the effectiveness of the proposed solutions and the contributions of features.
Chen, Y.-L.; Liu, Y.-H.; Ho, W.-L.: ¬A text mining approach to assist the general public in the retrieval of legal documents (2013) 0.00
```
0.0012212053 = product of:
  0.012212053 = sum of:
    0.012212053 = product of:
      0.03663616 = sum of:
        0.03663616 = weight(_text_:problem in 521) [ClassicSimilarity], result of:
          0.03663616 = score(doc=521,freq=2.0), product of:
            0.1302053 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.03067635 = queryNorm
            0.28137225 = fieldWeight in 521, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.046875 = fieldNorm(doc=521)
      0.33333334 = coord(1/3)
  0.1 = coord(1/10)
```
Abstract

Applying text mining techniques to legal issues has been an emerging research topic in recent years. Although some previous studies focused on assisting professionals in the retrieval of related legal documents, they did not take into account the general public and their difficulty in describing legal problems in professional legal terms. Because this problem has not been addressed by previous research, this study aims to design a text-mining-based method that allows the general public to use everyday vocabulary to search for and retrieve criminal judgments. The experimental results indicate that our method can help the general public, who are not familiar with professional legal terms, to acquire relevant criminal judgments more accurately and effectively.
Qiu, X.Y.; Srinivasan, P.; Hu, Y.: Supervised learning models to predict firm performance with annual reports : an empirical study (2014) 0.00
```
0.0012212053 = product of:
  0.012212053 = sum of:
    0.012212053 = product of:
      0.03663616 = sum of:
        0.03663616 = weight(_text_:problem in 1205) [ClassicSimilarity], result of:
          0.03663616 = score(doc=1205,freq=2.0), product of:
            0.1302053 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.03067635 = queryNorm
            0.28137225 = fieldWeight in 1205, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.046875 = fieldNorm(doc=1205)
      0.33333334 = coord(1/3)
  0.1 = coord(1/10)
```
Abstract

Text mining and machine learning methodologies have been applied toward knowledge discovery in several domains, such as biomedicine and business. Interestingly, in the business domain, the text mining and machine learning community has minimally explored company annual reports with their mandatory disclosures. In this study, we explore the question "How can annual reports be used to predict change in company performance from one year to the next?" from a text mining perspective. Our article contributes a systematic study of the potential of company mandatory disclosures using a computational viewpoint in the following aspects: (a) We characterize our research problem along distinct dimensions to gain a reasonably comprehensive understanding of the capacity of supervised learning methods in predicting change in company performance using annual reports, and (b) our findings from unbiased systematic experiments provide further evidence about the economic incentives faced by analysts in their stock recommendations and speculations on analysts having access to more information in producing earnings forecast.
Sarnikar, S.; Zhang, Z.; Zhao, J.L.: Query-performance prediction for effective query routing in domain-specific repositories (2014) 0.00
```
0.0012212053 = product of:
  0.012212053 = sum of:
    0.012212053 = product of:
      0.03663616 = sum of:
        0.03663616 = weight(_text_:problem in 1326) [ClassicSimilarity], result of:
          0.03663616 = score(doc=1326,freq=2.0), product of:
            0.1302053 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.03067635 = queryNorm
            0.28137225 = fieldWeight in 1326, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.046875 = fieldNorm(doc=1326)
      0.33333334 = coord(1/3)
  0.1 = coord(1/10)
```
Abstract

The effective use of corporate memory is becoming increasingly important because every aspect of e-business requires access to information repositories. Unfortunately, less-than-satisfying effectiveness in state-of-the-art information-retrieval techniques is well known, even for some of the best search engines such as Google. In this study, the authors resolve this retrieval ineffectiveness problem by developing a new framework for predicting query performance, which is the first step toward better retrieval effectiveness. Specifically, they examine the relationship between query performance and query context. A query context consists of the query itself, the document collection, and the interaction between the two. The authors first analyze the characteristics of query context and develop various features for predicting query performance. Then, they propose a context-sensitive model for predicting query performance based on the characteristics of the query and the document collection. Finally, they validate this model with respect to five real-world collections of documents and demonstrate its utility in routing queries to the correct repository with high accuracy.
Ma, Z.; Sun, A.; Cong, G.: On predicting the popularity of newly emerging hashtags in Twitter (2013) 0.00
```
0.0010176711 = product of:
  0.010176711 = sum of:
    0.010176711 = product of:
      0.03053013 = sum of:
        0.03053013 = weight(_text_:problem in 967) [ClassicSimilarity], result of:
          0.03053013 = score(doc=967,freq=2.0), product of:
            0.1302053 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.03067635 = queryNorm
            0.23447686 = fieldWeight in 967, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.0390625 = fieldNorm(doc=967)
      0.33333334 = coord(1/3)
  0.1 = coord(1/10)
```
Abstract

Because of Twitter's popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i.e., Naïve bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.

Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.00

6.927037E-4 = product of:
  0.0069270367 = sum of:
    0.0069270367 = product of:
      0.02078111 = sum of:
        0.02078111 = weight(_text_:22 in 668) [ClassicSimilarity], result of:
          0.02078111 = score(doc=668,freq=2.0), product of:
            0.10742335 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03067635 = queryNorm
            0.19345059 = fieldWeight in 668, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=668)
      0.33333334 = coord(1/3)
  0.1 = coord(1/10)

Date: 22. 3.2013 19:43:01

Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.00

6.927037E-4 = product of:
  0.0069270367 = sum of:
    0.0069270367 = product of:
      0.02078111 = sum of:
        0.02078111 = weight(_text_:22 in 1605) [ClassicSimilarity], result of:
          0.02078111 = score(doc=1605,freq=2.0), product of:
            0.10742335 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03067635 = queryNorm
            0.19345059 = fieldWeight in 1605, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=1605)
      0.33333334 = coord(1/3)
  0.1 = coord(1/10)

Source: Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22

Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019) 0.00

6.927037E-4 = product of:
  0.0069270367 = sum of:
    0.0069270367 = product of:
      0.02078111 = sum of:
        0.02078111 = weight(_text_:22 in 5011) [ClassicSimilarity], result of:
          0.02078111 = score(doc=5011,freq=2.0), product of:
            0.10742335 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.03067635 = queryNorm
            0.19345059 = fieldWeight in 5011, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0390625 = fieldNorm(doc=5011)
      0.33333334 = coord(1/3)
  0.1 = coord(1/10)

Date: 7. 3.2019 16:32:22

Search (14 results, page 1 of 1)

Authors

Themes