Visualising Sentiments in Financial Texts?

Ahmad, Khurshid; Almas, Yousif

doi:10.1109/iv.2005.143

Cited by 18 publications

(11 citation statements)

References 9 publications

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“…Ahmad and Almas [24] aimed at extracting sentiments from financial texts. The motivation of the work was to find some grammars or rules describing common and frequent Arabic patterns that are usually used in financial news to report changes in object values like shares value.…”

Section: Related Workmentioning

confidence: 99%

A study of the effects of preprocessing strategies on sentiment analysis for Arabic text

Duwairi

El-Orfali

2014

Journal of Information Science

147

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Sentiment analysis has drawn considerable interest among researchers owing to the realization of its fascinating commercial and business benefits. This paper deals with sentiment analysis in Arabic text from three perspectives. First, several alternatives of text representation were investigated. In particular, the effects of stemming, feature correlation and n-gram models for Arabic text on sentiment analysis were investigated. Second, the behaviour of three classifiers, namely, SVM, Naive Bayes, and K-nearest neighbour classifiers, with sentiment analysis was investigated. Third, the effects of the characteristics of the dataset on sentiment analysis were analysed. To this end, we applied the techniques proposed in this paper to two datasets; one was prepared in-house by the authors and the second one is freely available online. All the experimentation was done using Rapidminer. The results show that our selection of preprocessing strategies on the reviews increases the performance of the classifiers.

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Section: Related Workmentioning

confidence: 99%

A study of the effects of preprocessing strategies on sentiment analysis for Arabic text

Duwairi

El-Orfali

2014

Journal of Information Science

147

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“…Based on this analysis, the users can obtain information about the local business in the language they understand, and therefore provide a better search experience for the Middle East region. One of the earlier researches in sentiment analysis is presented in [23]. This research focuses on automatic extraction of sentiment patterns from texts in Arabic language.…”

Section: Fig 1 Opinion Analysis Schemamentioning

confidence: 99%

An Evaluation of Sentiment Analysis and Classification Algorithms for Arabic Textual Data

Mostafa¹

2017

IJCA

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Sentiment analysis is a recent advance in text mining applications for analyzing textual data according to orientation of human comments to determine whether they are positive, negative, or neutral. Different data mining techniques and algorithms such as support vector machine, naïve Bayes, decision tree, k-nearest neighbor and other techniques are used for analyzing textual data. These techniques are evaluated based on Arabic language due to its richness and diversity that can lead to difficulties in analyzing and mining large number of morphological and linguistic words that can lead to different meaning. This research provides sophisticated categorization of most or all recent articles according to the algorithms used in analyzing sentiment data. A comparison table for the proposed algorithms is presented that explains each algorithm and its use in mining and analysis of Arabic textual data and provides different evaluation for each sentiment analysis and classification algorithm according to different categories such as sentiment type, feature selection, sentiment polarity, domain oriented , data scope and data source, algorithm used in the sentiment analysis or classification, and the best algorithm result during the analysis and mining process. The experimental results explain that support vector machine algorithm presents high accuracy with approximately 77% when compared to other text mining algorithms. Different algorithms of sentiment analysis and classifications are evaluated based on their use in Arabic language which has not been evaluated before.

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“…It has a unique structure and its own rules. For example, sentences are written from right to left, there are no capital letters, and there are a number of grammatical rules [11].…”

Section: A Sentiment Analysis In the Arabic Languagementioning

confidence: 99%

“…In the first phase, frequent patterns are mined with regard to the threshold minimum support. In the second phase, association rules are created with regard to the confidence threshold and minimum confidence [11].…”

Section: B Text Association Rulesmentioning

confidence: 99%

Visualising Arabic Sentiments and Association Rules in Financial Text

AL-Rubaiee¹,

Qiu²,

Li³

2017

ijacsa

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Abstract-Text mining methods involve various techniques, such as text categorization, summarisation, information retrieval, document clustering, topic detection, and concept extraction. In addition, because of the difficulties involved in text mining, visualisation techniques can play a paramount role in the analysis and pre-processing of textual data. This paper will present two novel frameworks for the classification and extraction of the association rules and the visualisation of financial Arabic text in order to realize both the general structure and the sentiment within an accumulated corpus. However, mining unstructured data with natural language processing (NLP) and machine learning techniques can be arduous, especially where the Arabic language is concerned, because of limited research in this area. The results show that our frameworks can readily classify Arabic tweets. Furthermore, they can handle many antecedent text association rules for the positive class and the negative class.

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Visualising Sentiments in Financial Texts?

Cited by 18 publications

References 9 publications

A study of the effects of preprocessing strategies on sentiment analysis for Arabic text

A study of the effects of preprocessing strategies on sentiment analysis for Arabic text

An Evaluation of Sentiment Analysis and Classification Algorithms for Arabic Textual Data

Visualising Arabic Sentiments and Association Rules in Financial Text

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