Research on Semantic Text Mining Based on Domain Ontology

Li-hua, Jiang; Zhang, Hongbin; Yang, Xiaorong; Xie, Naiming

doi:10.1007/978-3-642-36124-1_40

Cited by 7 publications

(5 citation statements)

References 11 publications

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“…In the recent debates, text mining definition and algorithms involved in its implementation has been equivocally discussed (Hudaefi et al , 2021; Hudaefi and Badeges, 2021; Jiang et al , 2013; Villarroel Ordenes and Zhang, 2019). However, it has been understood as a specialised form of data mining to extract information and discover knowledge from vast amounts of unstructured textual data (Cheerkoot-Jalim and Khedo, 2020; Jiang et al , 2014; Justicia De La Torre et al , 2018; Usai et al , 2018).…”

Section: Methodsmentioning

confidence: 99%

Mining netizen’s opinion on cryptocurrency: sentiment analysis of Twitter data

Hassan

Hudaefi

Caraka

2021

SEF

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Purpose This paper aims to explore netizen’s opinions on cryptocurrency under the lens of emotion theory and lexicon sentiments analysis via machine learning. Design/methodology/approach An automated Web-scrapping via RStudio is performed to collect the data of 15,000 tweets on cryptocurrency. Sentiment lexicon analysis is done via machine learning to evaluate the emotion score of the sample. The types of emotion tested are anger, anticipation, disgust, fear, joy, sadness, surprise, trust and the two primary sentiments, i.e. negative and positive. Findings The supervised machine learning discovers a total score of 53,077 sentiments from the sampled 15,000 tweets. This score is from the artificial intelligence evaluation of eight emotions, i.e. anger (2%), anticipation (18%), disgust (1%), fear (3%), joy (15%), sadness (3%), surprise (7%), trust (15%) and the two sentiments, i.e. negative (4%) and positive (33%). The result indicates that the sample primarily contains positive sentiments. This finding is theoretically significant to measure the emotion theory on the sampled tweets that can best explain the social implications of the cryptocurrency phenomenon. Research limitations/implications This work is limited to evaluate the sampled tweets’ sentiment scores to explain the social implication of cryptocurrency. Practical implications The finding is necessary to explain the recent phenomenon of cryptocurrency. The positive sentiment may describe the increase in investment in the decentralised finance market. Meanwhile, the anticipation emotion may illustrate the public’s reaction to the bubble prices of cryptocurrencies. Social implications Previous studies find that the social signals, e.g. word-of-mouth, netizens’ opinions, among others, affect the cryptocurrencies’ movement prices. This paper helps explain the social implications of such dynamic of pricing via sentiment analysis. Originality/value This study contributes to theoretically explain the implications of the cryptocurrency phenomenon under the emotion theory. Specifically, this study shows how supervised machine learning can measure the emotion theory from data tweets to explain the implications of cryptocurrencies.

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Section: Methodsmentioning

confidence: 99%

Mining netizen’s opinion on cryptocurrency: sentiment analysis of Twitter data

Hassan

Hudaefi

Caraka

2021

SEF

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“…the terms "Realism", "author", "Russian"). While other text mining approaches rely on the analysis of domain knowledge (Jiang, Zhang, Yang, & Xie, 2012) or relevant morph syntactic patterns (Shekar & Shoba, 2009) to extract terms in the mining process, here we used a simpler method that is based on the frequency with which words and compound terms appear in the text. The method relies on a parameter n to extract the compound terms with more than one word.…”

Section: Forumminer: Design and Implementationmentioning

confidence: 99%

Analysis of the Relevance of Posts in Asynchronous Discussions

Azevedo

Reategui

Behar

2014

IJELL

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This paper presents ForumMiner, a tool for the automatic analysis of students' posts in asynchronous discussions. ForumMiner uses a text mining system to extract graphs from texts that are given to students as a basis for their discussion. These graphs contain the most relevant terms found in the texts, as well as the relationships between them. By comparing these graphs with the students' writings, and using other information such as number and structure of posts, the tool is able to assess the relevance of each student's contributions. A group of 18 teachers was consulted about the most important aspects to be considered in the analysis of posts, and the tool has been built according to these aspects. Results of an experiment involving the analysis of 682 posts showed that the average of the ratings given to these posts by teachers was very close to those provided by the automated tool. Such results demonstrate the tool's potential to assist teachers in evaluating student participation in asynchronous discussions.

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“…Other approaches have been taken in these hard-to classify domains. There have been studies on the incorporation of domain knowledge by the selection and creation of cross-referencing query (Zhang et al, 2012), and domain documents (Dinh & Tamine, 2012), or reweighing of features using related information such as ontologies (Jiang et al, 2013) or user feedback (Ivanov et al, 2012). Domain knowledge has also been incorporated into text classifiers by modification of the classifiers to include prior results (Zheng et al, 2012).…”

Section: Figure 2 Steps In a Classification Processmentioning

confidence: 99%

Temporal and Contextual Evaluation of Background Knowledge Discovery for Short Text Classification

Taksa

Zelikovitz

Spink

2012

International Journal of Organizational and Collective Intelligence

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Background Knowledge (BK) plays an essential role in machine learning for short-text and non-topical classification. In this paper the authors present and evaluate two Information Retrieval techniques used to assemble four sets of BK in the past seven years. These sets were applied to classify a commercial corpus of search queries by the apparent age of the user. Temporal and contextual evaluations were used to examine results of various classification scenarios providing insight into choice, significance and range of tuning parameters. The evaluations also demonstrated the impact of the dynamic Web collection on classification results, and the advantages of Automatic Query Expansion (AQE) vs. basic search. The authors discuss other results of this research and its implications on the advancement of short text classification.

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Research on Semantic Text Mining Based on Domain Ontology

Cited by 7 publications

References 11 publications

Mining netizen’s opinion on cryptocurrency: sentiment analysis of Twitter data

Mining netizen’s opinion on cryptocurrency: sentiment analysis of Twitter data

Analysis of the Relevance of Posts in Asynchronous Discussions

Temporal and Contextual Evaluation of Background Knowledge Discovery for Short Text Classification

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