A semantic enhanced approach for online hotspot forums detection

Preethi, T.; Devi, Kamalini; Bhaskaran, V. Murali

doi:10.1109/icrtit.2012.6206785

Cited by 7 publications

(8 citation statements)

References 8 publications

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“…In this paper sentiment analysis is done using SentiStrenth Dictionary that shows polarity for given posts. This approach gives highly consistent result than previous approach [1].…”

Section: IIIsupporting

confidence: 67%

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A survey on online forum hotspot detection

Kazi

Joshi

Machchhar

2014

2014 IEEE International Conference on Computational Intelligence and Computing Research

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Social network, online forums, blogs and various sites where people can hold conversation in the form of messages, is recently become a more valuable resource for mining in various fields like customer relationship management, public opinion tracking and other text mining entities. The knowledge obtained from these public forums is extremely valuable for marketing research companies. This paper shows various tools available for crawling data from online forums. In first part emotional polarity for extracted text is obtained. In second part combined approach using K-means clustering and various classification algorithm like SVM, J48 (Decision Tree), Naïve Bayes and PSO(Particle Swarm Optimization)-SVM is applied to detect weather given forum is hotspot or non-hotspot for given time window. Among them PSO-SVM gives better result with K-means compare to other classifiers.

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“…In this paper sentiment analysis is done using SentiStrenth Dictionary that shows polarity for given posts. This approach gives highly consistent result than previous approach [1].…”

Section: IIIsupporting

confidence: 67%

“…So extracting information from web pages is difficult compare to documents. In this information age customer review or opinion plays vital role in mining process [1,2,3,5].…”

Section: Introductionmentioning

confidence: 99%

A survey on online forum hotspot detection

Kazi

Joshi

Machchhar

2014

2014 IEEE International Conference on Computational Intelligence and Computing Research

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“…First, we analyse and discuss the situation of predicting the same data set using different methods. Preethi et al [29] proposed two text mining approaches such as K-means clustering and support vector machine with particle swarm optimization (PSO-SVM) classification algorithms, which can be used to group into two forum clusters forming hotspot forums and non-hotspot forums within each time window. The data that they have collected for empirical studies are from forums.digital point.com.…”

Section: Comparison and Discussion Of Prediction Rate Using Differentmentioning

confidence: 99%

“…They achieve very moderate prediction rate from 50% to 80%. Preethi et al [29] detected online hotspot forums by computing the sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes sentiment score for each word or phrase of text.…”

Section: Related Workmentioning

confidence: 99%

A study of hotspot data prediction model in I/O workloads

Yang

Tan

Xie

et al. 2013

International Journal of Computer Mathematics

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There is a notable characteristic of the data access pattern: 80% I/O requests only access 20% data. This feature brings about the concept of hotspot data, which refer to the data in the most frequent requested area. The access to these hotspot data has direct influence upon the performance of the storage system's applications. Therefore, how to predict hotspot data is a critical research focus in the optimization of the storage system. In this paper, we propose a hotspot data prediction model based on a Zipf-like distribution, which can estimate and dynamically adjust parameters according to the present statistics of I/O access. We classify the hotspot data from every trace, and analyse the prediction rate through the classified hotspot data's characteristic. We synthesize the analysis results in different time granularities and hotspot data prediction queue lengths. Finally, we use block I/O traces to discuss the effectiveness of this model. The discussion and analysis results indicate that this model can predict the hotspot data efficiently.

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“…It comprises three steps: topic extraction, document clustering, and clustering description. K-means clustering and support vector machine method are usually adopted to group clusters (Li & Wu 2010;Preethi et al 2012). Cui et al used HDP to analyze various evolution patterns that emerge from multiple topics (Cui et al 2011).…”

Section: Related Workmentioning

confidence: 99%

Towards better understanding of hot topics in online learning communities

Zheng

Bao

et al. 2015

Smart Learn. Environ.

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Online learning communities provide open workspaces allowing learners to share information, exchange ideas, address problems and discuss on specific themes. But with the continuously increasing artifacts in online communities, learners feel it difficult to quickly and easily gain an insight into a certain theme. To facilitate and support learners have a better understanding of the communication focus, this paper presents an approach to discover the hot topics and patterns of topics evolutions in online learning communities. Firstly, hot terms are extracted based on three features: the frequency of the terms used in the document collection; the location of the terms within a document; the breadth of terms distribution in the document collection. Then a term association network is constructed by computing the terms co-occurrence and distance between them. Finally, an algorithm is proposed to select the kernel term and its associated terms as term clusters to represent the hot topics with multi-facets expression. Two case studies on real datasets are conducted to demonstrate the effectiveness and usefulness of term cluster in helping users better understand hot topics in online learning communities. Potential applications in learning scenarios are also discussed.

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A semantic enhanced approach for online hotspot forums detection

Cited by 7 publications

References 8 publications

A survey on online forum hotspot detection

A survey on online forum hotspot detection

A study of hotspot data prediction model in I/O workloads

Towards better understanding of hot topics in online learning communities

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