Towards Creation of Linguistic Resources for Bilingual Sentiment Analysis of Twitter Data

Javed, Iqra; Afzal, Hammad; Majeed, Azeem; Khan, Behram

doi:10.1007/978-3-319-07983-7_32

Cited by 14 publications

(6 citation statements)

References 3 publications

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“…(i) Classification of topic and language (ii) semi-automatic building of lexicons using current Senti-strength and Wordnet. (iii) Tweets are evaluated and the degree of sentiments is calculated for each tweet using a bilingual sentiment lexicon (Javed, Afzal, Majeed & Khan, 2014). In another study, a linguistic reservoir was suggested, consisting of sentiment scores of Roman Urdu text by conducting a spatial analysis of bilingual (Urdu and English) tweets under the theme of General Elections 2013.…”

Section: Brief Review On Roman Urdu Corpora and Sentiment Analysismentioning

confidence: 99%

Deep sentiments in Roman Urdu text using Recurrent Convolutional Neural Network model

Mahmood

Safder

Nawab

et al. 2020

Information Processing & Management

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Section: Brief Review On Roman Urdu Corpora and Sentiment Analysismentioning

confidence: 99%

Deep sentiments in Roman Urdu text using Recurrent Convolutional Neural Network model

Mahmood

Safder

Nawab

et al. 2020

Information Processing & Management

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“…[117] [118] [119] [46, 120, In addition to the lexicons mentioned above, 19 studies used lexicons that they created as part of their work or specifically focused on creating social opinion mining lexicons, such as [118] who created the AFINN word list for sentiment analysis in microblogs, [111] who built a bilingual sentiment lexicon for English and Roman Urdu, [65] the creators of the first Italian sentiment thesaurus, [106] for Chinese sentiment analysis and [115] for sentiment analysis on Twitter. These lexicons varied from social media focused lexicons [134,104,86], to sentiment and/or emoticon lexicons [66,109,91,120,123,98,74,100] and extensions of existing state-of-the-art lexicons [107,108,110], such as [107] who extended HowNetSenti with words manually collected from the internet, and [108] who built a sentiment lexicon from SenticNet 49 and SentiWordNet for slang words and acronyms.…”

Section: Lexicon (Lx)mentioning

confidence: 99%

“…The Politics domain is the dominant application area with 45 studies applying social opinion mining on different events, namely elections [453,103,197,50,121,87,88,157,53,327,244,539,421,422,337,203,168,368,442,222,520,178,212,184,117,511,190,441,310,406,82], reforms, such as equality marriage [130], debates [180], referendums [241,540], political parties or politicians [60,111,466], and political events, such as terrorism, protests, uprisings and riots [420,437,330,556,205,248,188].…”

Section: Application Areas Of Social Opinion Miningmentioning

confidence: 99%

Over a Decade of Social Opinion Mining: A Systematic Review

Cortis,

Davis

2020

Preprint

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Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing and other

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“…There have also been a few studies that investigate possible applications such as bilingual classification and sentiment analysis [33]; transliteration [13], [34]; word prediction [35]; and tagging parts of speech etc.…”

Section: Roman Urdu Corporamentioning

confidence: 99%

Analysing Emergent Users' Text Messages Data and Exploring its Benefits

Alsallakh¹,

Rextin²,

Kakakhail³

et al. 2018

Preprint

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While users in the developed world can choose to adopt the technology that suits their needs, the emergent users cannot afford this luxury, hence, they adapt themselves to the technology that is readily available. When technology is designed, such as the mobile-phone technology, it is an implicit assumption that it would be adopted by the emergent users in due course. However, such user groups have different needs, and they follow different usage patterns as compared to users from the developed world. In this work, we target an emergent user base, i.e., users from a university in Pakistan, and analyse their texting behaviour on mobile phones. We see interesting results such as, the long-term linguistic adaptation of users in the absence of reasonable Urdu keyboards, the overt preference for communicating in Roman Urdu and the social forces related to textual interaction. We also present two case studies on how a single dataset can effectively help understand emergent users, improve usability of some tasks, and also help users perform previously difficult tasks with ease.

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Towards Creation of Linguistic Resources for Bilingual Sentiment Analysis of Twitter Data

Cited by 14 publications

References 3 publications

Deep sentiments in Roman Urdu text using Recurrent Convolutional Neural Network model

Deep sentiments in Roman Urdu text using Recurrent Convolutional Neural Network model

Over a Decade of Social Opinion Mining: A Systematic Review

Analysing Emergent Users' Text Messages Data and Exploring its Benefits

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