Rhetorical Representation and Vector Representation in Summarizing Arabic Text

Text Mining is a set of techniques that analyzes large masses of data, extract relations that are unknown beforehand, and provide solutions to help decision-making. Text mining had been used extensively to analyze English text. However, text mining has only been used recently in analyzing Arabic text. As a result the objective of this paper is to present the current state of Arabic text mining. A systematic review has been performed to collect the papers published on the analysis of Arabic text mining. More than one hundred papers were used in our review from different reliable sources, and then they were classified according to their specific domain, and classified again according to the specific techniques used. This paper also provides quantitative analysis of publications according to publication type, year, category, and contributors.

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“…Clustering [69], [70], [71], [72], [73], [74], [75] 7(6%) Summarization [76], [77] , [78], [79], [80], [81], [82] 7(6%)…”

Section: Techniquementioning

confidence: 99%

Arabic Text Mining a Systematic Review of the Published Literature 2002-2014

AlMahmoud

Al-Razgan

2015

2015 International Conference on Cloud Computing (ICCC)

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“…In this light, one of the key open research issues refers to the role of textual structure [12]. Structural aspects seem to be valuable for various Text Mining tasks [18,19,20,21], including SA [15,16,22,23,24], but this requires further study.…”

Section: Introductionmentioning

confidence: 99%

Rhetorical Structure Theory for polarity estimation: An experimental study

Chenlo

Hogenboom

Losada

2014

Data & Knowledge Engineering

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Sentiment Analysis tools often rely on counts of sentiment-carrying words, ignoring structural aspects of content. Natural Language Processing has been fruitfully exploited in Text Mining, but advanced discourse processing is still non pervasive for mining opinions. Some studies, however, extracted opinions based on the discursive role of text segments. The merits of such computationally intensive analyses have thus far been assessed in very specific, small-scale scenarios. In this paper, we investigate the usefulness of Rhetorical Structure Theory in various Sentiment Analysis tasks on different types of information sources. First, we demonstrate how to perform a large-scale ranking of individual blog posts in terms of their overall polarity, by exploiting the rhetorical structure of a few key evaluative sentences. In order to further validate our findings, we additionally explore the potential of Rhetorical Structure Theory in sentence-level polarity classification of news and product reviews. Our most valuable polarity classification features turn out to capture the way in which polar terms are used, rather than the sentimentcarrying words per se.

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