TOP-Rank: A Novel Unsupervised Approach for Topic Prediction Using Keyphrase Extraction for Urdu Documents

Amin, Ahmad; Rana, Toqir A.; Mian, Natash Ali; Iqbal, Muhammad Waseem; Khalid, Abbas; Alyas, Tahir; Tubishat, Mohammad

doi:10.1109/access.2020.3039548

Cited by 9 publications

(6 citation statements)

References 36 publications

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“…The rules used were manually crafted and a list of Urdu opinion words was utilized to identify opinion words. Amin et al [27] used a ranking-based model to identify titles of the documents for the Urdu language. Rehmand et al [28] used opinion lexicons for the classification of sentences and identification of opinions.…”

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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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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“…Evaluation measures are important aspects of analysing the performance of any method. Mostly, all evaluation methods are influenced by the data's type [ 32 ]. This paper used T -Rec, K -Prec, and K -Rec due to their popularity in event detection [ 5 , 22 ].…”

Section: Experimental Evaluationmentioning

confidence: 99%

A Performance Comparison of Unsupervised Techniques for Event Detection from Oscar Tweets

Malik

Aslam

et al. 2022

Computational Intelligence and Neuroscience

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People’s lives are influenced by social media. It is an essential source for sharing news, awareness, detecting events, people’s interests, etc. Social media covers a wide range of topics and events to be discussed. Extensive work has been published to capture the interesting events and insights from datasets. Many techniques are presented to detect events from social media networks like Twitter. In text mining, most of the work is done on a specific dataset, and there is the need to present some new datasets to analyse the performance and generic nature of Topic Detection and Tracking methods. Therefore, this paper publishes a dataset of real-life event, the Oscars 2018, gathered from Twitter and makes a comparison of soft frequent pattern mining (SFPM), singular value decomposition and k-means (K-SVD), feature-pivot (Feat-p), document-pivot (Doc-p), and latent Dirichlet allocation (LDA). The dataset contains 2,160,738 tweets collected using some seed words. Only English tweets are considered. All of the methods applied in this paper are unsupervised. This area needs to be explored on different datasets. The Oscars 2018 is evaluated using keyword precision (K-Prec), keyword recall (K-Rec), and topic recall (T-Rec) for detecting events of greater interest. The highest K-Prec, K-Rec, and T-Rec were achieved by SFPM, but they started to decrease as the number of clusters increased. The lowest performance was achieved by Feat-p in terms of all three metrics. Experiments on the Oscars 2018 dataset demonstrated that all the methods are generic in nature and produce meaningful clusters.

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“…Similar to unsupervised approaches, these techniques are also usually focused on online product reviews. In study researchers [17] context-dependency have been used for aspect extraction and also opinions concurrently. To recognize opinion and their targets, they have used a bootstrapping technique that takes opinion lexicon for input to get aspects of the product.…”

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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TOP-Rank: A Novel Unsupervised Approach for Topic Prediction Using Keyphrase Extraction for Urdu Documents

Cited by 9 publications

References 36 publications

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

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

A Performance Comparison of Unsupervised Techniques for Event Detection from Oscar Tweets

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

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