Lexicon-based sentiment analysis for Urdu language

Rehman, Zia Ul; Bajwa, Imran Sarwar

doi:10.1109/intech.2016.7845095

Cited by 36 publications

(31 citation statements)

References 7 publications

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“…In this section, we present some of the latest works carried out in Urdu SA. An Urdu SA system is proposed by Rehman and Bajwa (2016) using different lexical resources such as English and Urdu lexicons. An efficient filtering strategy is proposed to discard irrelevant words by gaining an accuracy of more than 65%.…”

Section: Sentiment Analysis In Urdumentioning

confidence: 99%

Creating sentiment lexicon for sentiment analysis in Urdu: The case of a resource‐poor language

et al. 2019

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The sentiment analysis (SA) applications are becoming popular among the individuals and organizations for gathering and analysing user's sentiments about products, services, policies, and current affairs. Due to the availability of a wide range of English lexical resources, such as part‐of‐speech taggers, parsers, and polarity lexicons, development of sophisticated SA applications for the English language has attracted many researchers. Although there have been efforts for creating polarity lexicons in non‐English languages such as Urdu, they suffer from many deficiencies, such as lack of publically available sentiment lexicons with a proper scoring mechanism of opinion words and modifiers. In this work, we present a word‐level translation scheme for creating a first comprehensive Urdu polarity resource: “Urdu Lexicon” using a merger of existing resources: list of English opinion words, SentiWordNet, English–Urdu bilingual dictionary, and a collection of Urdu modifiers. We assign two polarity scores, positive and negative, to each Urdu opinion word. Moreover, modifiers are collected, classified, and tagged with proper polarity scores. We also perform an extrinsic evaluation in terms of subjectivity detection and sentiment classification, and the evaluation results show that the polarity scores assigned by this technique are more accurate than the baseline methods.

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Section: Sentiment Analysis In Urdumentioning

confidence: 99%

Creating sentiment lexicon for sentiment analysis in Urdu: The case of a resource‐poor language

et al. 2019

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“…In the next phase tokenization of Urdu text is performed. Tokenization is the way toward separating the given content into units called tokens [8,9]. The tokens might be words, numbers or punctuation marks.…”

Section: Urdu Sentiment Analysismentioning

confidence: 99%

“…The tokens might be words, numbers or punctuation marks. Tokenization does this assignment by finding word boundaries [8,9]. When tokens are created from the sentences then it is passed to the polarity identification stage [8,9].…”

Section: Urdu Sentiment Analysismentioning

confidence: 99%

“…However, very little research has been led in SA of the Urdu language [8]. The SA designed for English language cannot be utilized for Urdu language having different script and morphological structure [8,9]. Urdu has linguistic features that are diverse as well as contradicting to the English dialect [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…Like some other languages, Urdu online resource are in addition to getting popular with as individuals would like to partake impressions and convey opinions in their native languages [8,9]. From Literature study it is clear that techniques employed for counterpart languages cannot be adopted for handling Urdu language issues [8,9]. The main objective of this study is to report and investigate Urdu SA techniques and challenges.…”

Section: Introductionmentioning

confidence: 99%

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Urdu Sentiment Analysis

Khan¹,

Khan²,

Rahman³

et al. 2018

ijacsa

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Internet is the most significant source of getting up thoughts, surveys for a product, and reviews for any type of service or activity. A Bulky amount of reviews are produced on daily basis on the cyberspace about online products and objects. For example, many individuals share their remarks, reviews and feelings in their own language utilizing social media networks such as twitter and so on. Considering their colossal Quantity and size, it is exceedingly knotty to look at with and interpret specified surveys. Sentiment Analysis (SA) aims at extracting people's opinion, felling and thought from their reviews in social websites. SA has recently gained significant consideration, however the vast majority of the resources and frameworks constructed so far are tailored to English as well as English like Western languages. The requirement for designing frameworks for different dialects is expanding, particularly as blogging and micro-blogging sites are becoming popular. This paper presents a comprehensive review of approaches of Urdu sentiment analysis and outlines of relevant gaps in the literature.

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