Informational zooming: an interaction model for the graphical access to text knowledge bases

Thiel, Ulrich; Hammwöhner, Rainer

doi:10.1145/42005.42012

Cited by 11 publications

(5 citation statements)

References 16 publications

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“…This approach, lexically distributed text parsing, is dealt with in Section V. It has been developed and experimentally validated as the text understanding kernel of the TOPIC text condensation ~y s t e m ?~.~~ which provides summaries on different levels of conceptual abstraction together with facts acquired from the text and relevant passages of original texts within an integrated graphical retrieval environment. 39…”

Section: Introductionmentioning

confidence: 99%

Making understanders out of parsers: Semantically driven parsing as a key concept for realistic text understanding applications

Hahn

1989

Int. J. Intell. Syst.

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Semantically driven natural language parsers have found wide-spread application as a text processing methodology for knowledge-based information retrieval systems. It is argued that this parsing technique particularly corresponds to the requirements inherent to large-scale text analysis. Unfortunately, this approach suffers from several shortcomings which demand a thorough reformulation of its paradigm. Incorporating principles from conceptual analysis and word expert parsing in a model of lexically distributed text parsing, the focus of the modifications proposed in this article, is on a clean declarative separation of linguistic and other knowledge representation levels, abstraction mechanisms leading to a small collection of specification primitives for the parser, and an attempt to incorporate linguistic generalizations and modularization principles into the design of a semantic text grammar. A sample parse illustrates the operation and linguistic coverage of a lexically distributed text parser based on these theoretical considerations with respect to the semantic analysis of noun groups, simple assertional sentences, nominal anaphora, and textual ellipsis.

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Section: Introductionmentioning

confidence: 99%

Making understanders out of parsers: Semantically driven parsing as a key concept for realistic text understanding applications

Hahn

1989

Int. J. Intell. Syst.

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“…They discuss the success of online petitions from an agenda-setting view by examining the impact of popular petition features in the literature on such elements on the online petition. Furthermore, text mining has been addressed explicitly by Hill and Zein’s (1986, 1987) statistical data extraction from text and Thiel and Hammwöhner’s (1987) interface model for the graphical access to text sources.…”

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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“…They examine the influence of popular petition characteristics in the literature on such aspects of online petitions to explore the success of online petitions from an agenda-setting perspective. Furthermore, Hill and Zein’s (1986) statistical data extraction from text and Thiel and Hammwöhner’s (1987) interface paradigm for graphical access to text sources expressly address text mining. However, it is thought to be a specialized sort of data mining that extracts information and discovers knowledge from large volumes of unstructured textual data (Hossain and Rahman, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Customer sentiment analysis and prediction of halal restaurants using machine learning approaches

Hossain

Rahman

Uddin

et al. 2022

JIMA

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Purpose There is a strong prerequisite for organizations to analyze customer review behavior to evaluate the competitive business environment. The purpose of this study is to analyze and predict customer reviews of halal restaurants using machine learning (ML) approaches. Design/methodology/approach The authors collected customer review data from the Yelp website. The authors filtered the reviews of only halal restaurants from the original data set. Following cleaning, the filtered review texts were classified as positive, neutral or negative sentiments, and those sentiments were scored using the AFINN and VADER sentiment algorithms. Also, the current study applies four machine learning methods to classify each review toward halal restaurants into its sentiment class. Findings The experiment showed that most of the customer reviews toward halal restaurants were positive. The authors also discovered that all of the methods (decision tree, linear support vector machine, logistic regression and random forest classifier) can correctly classify the review text into sentiment class, but logistic regression outperforms the others in terms of accuracy. Practical implications The results facilitate halal restaurateurs in identifying customer review behavior. Social implications Sentiment and emotions, according to appraisal theory, form the basis for all interactions, facilitating cognitive functions and supporting prospective customers in making sense of experiences. Emotion theory also describes human affective states that determine motives and actions. The study looks at how potential customers might react to a halal restaurant’s consensus on social media based on reviewers’ opinions of halal restaurants because emotions can be conveyed through reviews. Originality/value This study applies machine learning approaches to analyze and predict customer sentiment based on the review texts toward halal restaurants.

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Informational zooming: an interaction model for the graphical access to text knowledge bases

Cited by 11 publications

References 16 publications

Making understanders out of parsers: Semantically driven parsing as a key concept for realistic text understanding applications

Making understanders out of parsers: Semantically driven parsing as a key concept for realistic text understanding applications

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

Customer sentiment analysis and prediction of halal restaurants using machine learning approaches

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