Effective Use of Evaluation Measures for the Validation of Best Classifier in Urdu Sentiment Analysis

Mukhtar, Neelam; Khan, Masood Alam; Chiragh, Nadia

doi:10.1007/s12559-017-9481-5

Cited by 27 publications

(17 citation statements)

References 24 publications

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“…The five classifiers along with their average values are shown in Table 7. From Table 7, it is clear that IBK is performing better than the other classifiers in terms of accuracy (Mukhtar et al, 2017).…”

Section: Supervised Machine Learning Approachmentioning

confidence: 96%

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Identification and handling of intensifiers for enhancing accuracy of Urdu sentiment analysis

et al. 2018

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Just like other languages, a large number of intensifiers are used in Urdu language. There may be a single occurrence of an intensifier or there may be multiple consecutive occurrences. While performing sentiment analysis of Urdu text, these intensifiers need special treatment for obtaining more accurate results, which is the main focus of this research work. A wide coverage Urdu sentiment lexicon is developed where intensifiers are identified and placed in a separate file. While developing Urdu sentiment analyser, rules are specifically formulated for assigning polarities to the intensifiers in text, if they are surrounded by the positive or negative words. Results show that the method proved to be effective in attaining the correct classification of Urdu sentences as positive, negative, or neutral, compared with traditional methods. Implementation of rules for intensifiers increased the accuracy of Urdu sentiment analyser from 78.33% to 83.42%, which is a statistically significant improvement. It is concluded that intensifiers cannot be ignored while performing sentiment analysis. Effective handling of intensifiers can significantly improve the performance of sentiment analyser.

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Section: Supervised Machine Learning Approachmentioning

confidence: 96%

“…From Table , it is clear that IBK is performing better than the other classifiers in terms of accuracy (Mukhtar et al, ).…”

Section: Computational Experimentsmentioning

confidence: 99%

Identification and handling of intensifiers for enhancing accuracy of Urdu sentiment analysis

et al. 2018

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“…Phrase based statistical machine translation is being done and conventional natural language processing techniques like done POS tagging [24], stemming [26], [24], [27], [28], tokenization, annotation [29], lemmatization [24], sentence boundary detection [24] and named entity recognition [24] are applied in the areas of sentiment analysis, handwriting recognition, opinion mining and plagiarism detection. Work done for the Urdu language has hugely relied on conventional natural language processing techniques [30], [31], [29] and the use of deep learning to address the problem of machine translation in Urdu is still in its inception.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Sequence to sequence networks for Roman-Urdu to Urdu transliteration

Alam

Hussain

2017

2017 International Multi-Topic Conference (INMIC)

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Neural Machine Translation models have replaced the conventional phrase based statistical translation methods since the former takes a generic, scalable, data-driven approach rather than relying on manual, hand-crafted features. The neural machine translation system is based on one neural network that is composed of two parts, one that is responsible for input language sentence and other part that handles the desired output language sentence. This model based on encoder-decoder architecture also takes as input the distributed representations of the source language which enriches the learnt dependencies and gives a warm start to the network. In this work, we transform Roman-Urdu to Urdu transliteration into sequence to sequence learning problem. To this end, we make the following contributions. We create the first ever parallel corpora of Roman-Urdu to Urdu, create the first ever distributed representation of Roman-Urdu and present the first neural machine translation model that transliterates text from Roman-Urdu to Urdu language. Our model has achieved the state-of-the-art results using BLEU as the evaluation metric. Precisely, our model is able to correctly predict sentences up to length 10 while achieving BLEU score of 48.6 on the test set. We are hopeful that our model and our results shall serve as the baseline for further work in the domain of neural machine translation for Roman-Urdu to Urdu using distributed representation.

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“…Manual annotation scheme mainly relies on building manual dictionaries by a group of linguists of the target language. Such lexicons are reliable; however, their development process is time-consuming and subject to annotator bias, and not used independently, but used with automated approaches to minimize the errors committed during the semantic orientation of sentiment words (Mukhtar, Khan, & Chiragh, 2017). The dictionary-based approach takes a list of initial seed words and expands it over the other lexical resources, such as WordNet and SentiWordNet (Asghar, Khan, Ahmad, Khan, & Kundi, 2015).…”

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confidence: 99%

“…The aforementioned approaches for sentiment lexicon generation are widely used for creating sentiment to process English text. Several studies (Afraz, Muhammad, & Martinez-Enriquez, 2011;Awais, 2012;Badaro, Baly, Hajj, Habash, & El-Hajj, 2014;Bakliwal, Arora, & Varma, 2012;Dashtipour et al, 2016;Dehkharghani, Saygin, Yanikoglu, & Oflazer, 2016) have been conducted to perform sentiment analysis in languages other than English; till date, most of the research efforts made in the area of sentiment analysis deal with English text (Mukhtar et al, 2017). This is due to the fact that extraction and analysis of sentiments from text need a rich collection of lexical resources of that language.…”

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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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Effective Use of Evaluation Measures for the Validation of Best Classifier in Urdu Sentiment Analysis

Cited by 27 publications

References 24 publications

Identification and handling of intensifiers for enhancing accuracy of Urdu sentiment analysis

Identification and handling of intensifiers for enhancing accuracy of Urdu sentiment analysis

Sequence to sequence networks for Roman-Urdu to Urdu transliteration

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

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