Extraction of Opinion Target Using Syntactic Rules in Urdu Text

Rana, Toqir A.; Bakht, Bahrooz; Afzal, Mehtab; Mian, Natash Ali; Iqbal, Muhammad Waseem; Khalid, Abbas; Naqvi, Muhammad Raza

doi:10.32604/iasc.2021.018572

Cited by 7 publications

(3 citation statements)

References 42 publications

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“…They also conducted feature ranking based on bipartite weighted network calculations. On the other hand, Rana et al (2021) utilized an opinion lexicon developed for the Urdu language to initially extract opinions and then extract features, ranking the related features based on the distance between features and opinions. This approach led to the identification of opinion-feature pairs.…”

Section: Related Workmentioning

confidence: 99%

Feature extraction from customer reviews using enhanced rules

Santhiran,

Varathan,

Chiam

2024

PeerJ Computer Science

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Opinion mining is gaining significant research interest, as it directly and indirectly provides a better avenue for understanding customers, their sentiments toward a service or product, and their purchasing decisions. However, extracting every opinion feature from unstructured customer review documents is challenging, especially since these reviews are often written in native languages and contain grammatical and spelling errors. Moreover, existing pattern rules frequently exclude features and opinion words that are not strictly nouns or adjectives. Thus, selecting suitable features when analyzing customer reviews is the key to uncovering their actual expectations. This study aims to enhance the performance of explicit feature extraction from product review documents. To achieve this, an approach that employs sequential pattern rules is proposed to identify and extract features with associated opinions. The improved pattern rules total 41, including 16 new rules introduced in this study and 25 existing pattern rules from previous research. An average calculated from the testing results of five datasets showed that the incorporation of this study’s 16 new rules significantly improved feature extraction precision by 6%, recall by 6% and F-measure value by 5% compared to the contemporary approach. The new set of rules has proven to be effective in extracting features that were previously overlooked, thus achieving its objective of addressing gaps in existing rules. Therefore, this study has successfully enhanced feature extraction results, yielding an average precision of 0.91, an average recall value of 0.88, and an average F-measure of 0.89.

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Section: Related Workmentioning

confidence: 99%

Feature extraction from customer reviews using enhanced rules

Santhiran,

Varathan,

Chiam

2024

PeerJ Computer Science

View full text Add to dashboard Cite

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“…However, several approaches focusing on the sentiment analysis task in the Urdu language domain have been proposed. Rana et al [26] proposed a rule-based model to identify opinions and their targets from the Urdu dataset. The rules used were manually crafted and a list of Urdu opinion words was utilized to identify opinion words.…”

Section: Sentiment Analysis In the Urdu Languagementioning

confidence: 99%

Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

Ullah¹,

Talib²,

Rana³

et al. 2022

Computers, Materials &Amp; Continua

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In the current era of the internet, people use online media for conversation, discussion, chatting, and other similar purposes. Analysis of such material where more than one person is involved has a spate challenge as compared to other text analysis tasks. There are several approaches to identify users' emotions from the conversational text for the English language, however regional or low resource languages have been neglected. The Urdu language is one of them and despite being used by millions of users across the globe, with the best of our knowledge there exists no work on dialogue analysis in the Urdu language. Therefore, in this paper, we have proposed a model which utilizes deep learning and machine learning approaches for the classification of users' emotions from the text. To accomplish this task, we have first created a dataset for the Urdu language with the help of existing English language datasets for dialogue analysis. After that, we have preprocessed the data and selected dialogues with common emotions. Once the dataset is prepared, we have used different deep learning and machine learning techniques for the classification of emotion. We have tuned the algorithms according to the Urdu language datasets. The experimental evaluation has shown encouraging results with 67% accuracy for the Urdu dialogue datasets, more than 10, 000 dialogues are classified into five emotions i.e., joy, fear, anger, sadness, and neutral. We believe that this is the first effort for emotion detection from the conversational text in the Urdu language domain.

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“…In a study [24], they have combined CRFs with three different types of language features to extract the aspects. These features include, word and POS feature, pulse sentence structure.…”

Section: Related Workmentioning

confidence: 99%

Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

Hussain¹,

Rana²,

Iftikhar³

et al. 2022

Computers, Materials &Amp; Continua

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In the field of sentiment analysis, extracting aspects or opinion targets from user reviews about a product is a key task. Extracting the polarity of an opinion is much more useful if we also know the targeted Aspect or Feature. Rule based approaches, like dependency-based rules, are quite popular and effective for this purpose. However, they are heavily dependent on the authenticity of the employed parts-of-speech (POS) tagger and dependency parser. Another popular rule based approach is to use sequential rules, wherein the rules formulated by learning from the user's behavior. However, in general, the sequential rule-based approaches have poor generalization capability. Moreover, existing approaches mostly consider an aspect as a noun or noun phrase, so these approaches are unable to extract verb aspects. In this article, we have proposed a multi-layered rule-based (ML-RB) technique using the syntactic dependency parser based rules along with some selective sequential rules in separate layers to extract noun aspects. Additionally, after rigorous analysis, we have also constructed rules for the extraction of verb aspects. These verb rules primarily based on the association between verb and opinion words. The proposed multi-layer technique compensates for the weaknesses of individual layers and yields improved results on two publicly available customer review datasets. The F1 score for both the datasets are 0.90 and 0.88, respectively, which are better than existing approaches. These improved results can be attributed to the application of sequential/syntactic rules in a layered manner as well as the capability to extract both noun and verb aspects.

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Extraction of Opinion Target Using Syntactic Rules in Urdu Text

Cited by 7 publications

References 42 publications

Feature extraction from customer reviews using enhanced rules

Feature extraction from customer reviews using enhanced rules

Deep Learning and Machine Learning-Based Model for Conversational Sentiment Classification

Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

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