A Brief Tutorial on How to Extract Information from User-Generated Content (UGC)

Egger, Marc; Lang, Andre

doi:10.1007/s13218-012-0224-1

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Second, THC-DAT could help document analysis researches to gather more user data. Many document analysis techniques rely on mining user-involved data stemmed from user-generated content or user behavior log, such as folksonomy, user preference learning and evaluating applications (Agosti et al , 2012; Egger and Lang, 2012; Francisco et al , 2012; Lin et al , 1999). However, the difficulty to complete a task has a negative impact on the willingness of participants (Smith, 1999).…”

Section: Discussionmentioning

confidence: 99%

THC-DAT helps in reading a multi-topic document

Chen

Wang

et al. 2016

LHT

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Purpose With a mass of electronic multi-topic documents available, there is an increasing need for evaluating emerging analysis tools to help users and digital libraries analyze these documents better. The purpose of this paper is to evaluate the effectiveness, efficiency and user satisfaction of THC-DAT, a within-document analysis tool, in reading a multi-topic document. Design/methodology/approach The authors reviewed related literature first, then performed a user-centered, comparative evaluation of two within-document analysis tools, THC-DAT and BOOKMARK. THC-DAT extracts a topic hierarchy tree using hierarchical latent Dirichlet allocation (hLDA) method and takes the context information into account. BOOKMARK provides similar functionality to the Table of Contents bookmarks in Adobe Reader. Three novel kinds of tasks were devised for participants to finish on two tools, with objective results to assess reading effectiveness and efficiency. And post-system questionnaires were employed to obtain participants’ subjective judgments about the tools. Findings The results confirm that THC-DAT is significantly more effective than BOOKMARK, while not inferior in efficiency. There is some evidence that suggests THC-DAT can slow down the process of approaching cognitive overload and improve users’ willingness to undertake difficult task. Based on qualitative data from questionnaires, the results indicate that users were more satisfied when using THC-DAT than BOOKMARK. Practical implications Adopting THC-DAT in digital libraries or electrical document reading systems contributes to promoting users’ reading performance, willingness to undertake difficult task and general satisfaction. Moreover, THC-DAT is of great value to addressing cognitive overload problem in the information retrieval field. Originality/value This paper evaluates a novel within-document analysis tool in analyzing a multi-topic document, and proved that this tool is superior to the benchmark in effectiveness and user satisfaction, and not inferior in efficiency.

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Section: Discussionmentioning

confidence: 99%

THC-DAT helps in reading a multi-topic document

Chen

Wang

et al. 2016

LHT

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“…5,6 Besides, the refinement of textual content from UGC is very important to excavate the content extensively. 7 Hence, most of the prior researches focused on sentiment analysis to extract the textual content from the UGC 8 .…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis of social media data based on chaotic coyote optimization algorithm based time weight‐AdaBoost support vector machine approach

Dangi

Bhagat

Dixit

2021

Concurrency and Computation

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Sentiment analysis or opinion mining is exploited in business, customer services, and so forth, where people provide their opinions in the form of reviews. However, the people's opinions are in a perplexing form such as, sarcasm, irony, and implied meaning which can cause an impact on sentiment analysis. The only way to analyze these words is through context. Nevertheless, there still exist some issues, to tackle those issues, a lot of research has been conducted by focusing the feature engineering. However, the optimized output has not been acquired yet. Hence, we propose a novel method known as chaotic coyote optimization algorithm (COA) based time weight-AdaBoost support vector machine (SVM) approach which can be used to attain the precise classifications in context. The proposed time weight-AdaBoost SVM can be used to circumvent the drift concept issues and can be utilized for the perfect learning of data for further classifications. Further, the class imbalance issues can be overcome by adopting a modified CO algorithm, that is, the chaotic COA. Furthermore, the proposed work performance is analyzed with state-of-art works such as DICE, ABCDM, and SVM approaches. The comparative analysis shows that our proposed work classifies the social media content acquired from Twitter more accurately than the other works. Thus our work outperforms all the existing approaches in terms of accuracy, precision, recall, and F1 score.

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