Product Quality Assessment using Opinion Mining in Persian Online Shopping

HosseinzadehBendarkheili, Fatemeh; MohammadiBaghmolaei, Rezvan; Ahmadi, Ali

doi:10.1109/iraniancee.2019.8786611

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…The results show a significant performance improvement of the Urdu sentiment analyzer from 83% to 89%. Hossein et al [23] introduce a lexicon-based method for SA in the Persian language using a dataset of mobile reviews. The authors extract the aspects from the reviews using the combination of 'noun adjective' pair or 'nouns adverbs adjective' pair using a lexicon.…”

Section: A Lexicon-based Approachmentioning

confidence: 99%

Deep Learning Based Cross Domain Sentiment Classification for Urdu Language

Altaf

Jamal

et al. 2022

IEEE Access

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Sentiment analysis is a widely researched area due to its various applications in customer services, brand monitoring, and market research. Automatic sentiment classification is an important but challenging task. Contrary to the English language, sentiment analysis for low-resource languages like Urdu is an under-explored research area. Most of the work on sentiment analysis in the Urdu language is domain-dependent where models are mostly trained and tested on the same dataset on limited domains. However, sentiments in different domains are expressed differently, and manually annotating the datasets for all possible domains is unfeasible. Training a sentiment classifier using annotated data on one domain and testing it on another domain results in poor performance as the terms appearing in the source domain (training data) might not appear in the target (testing data) domain. In this paper, we present a baseline method for cross-domain sentiment analysis in the Urdu language using two different domains. Feature extraction is performed using n-grams and word embedding techniques. Sentiment classification is performed using machine learning and deep learning classifiers. The proposed method achieves an accuracy, precision, recall, and F1 scores of 0.77, 0.83, 0.68, and 0.75, respectively.INDEX TERMS Cross-domain sentiment analysis; deep learning; Urdu language processing; feature engineering

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Section: A Lexicon-based Approachmentioning

confidence: 99%

Deep Learning Based Cross Domain Sentiment Classification for Urdu Language

Altaf

Jamal

et al. 2022

IEEE Access

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“…The existing work on Persian sentiment analysis are mainly focused on document-level sentiment identification which ignores the nuanced judgements with respect to granular elements in the context (HosseinzadehBendarkheili et al, 2019;Sharami et al, 2020, inter alia). Moreover, the majority of such resources, such as MirasOpinion (Ashrafi Asli et al, 2020), not only lack intensity labels, but also are limited to binary or ternary sentiment classes.…”

Section: Related Workmentioning

confidence: 99%

“…There is a rich set of works on Persian sentiment analysis. We build upon these works and differ from them in the following manners: (a) The existing work mainly focuses on document-level sentiment identification which does not capture the nuanced judgments with respect to aspects and entities of the context (HosseinzadehBendarkheili et al, 2019;Sharami et al, 2020, inter alia). In addition to such document-level annotations, we provide aspect-level sentiment annotations ( §3.2.3).…”

Section: Related Workmentioning

confidence: 99%

ParsiNLU: A Suite of Language Understanding Challenges for Persian

Khashabi

Cohan

Shakeri

et al. 2021

Transactions of the Association for Computational Linguistics

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Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English. This work focuses on Persian language, one of the widely spoken languages in the world, and yet there are few NLU datasets available for this language. The availability of high-quality evaluation datasets is a necessity for reliable assessment of the progress on different NLU tasks and domains. We introduce ParsiNLU, the first benchmark in Persian language that includes a range of language understanding tasks—reading comprehension, textual entailment, and so on. These datasets are collected in a multitude of ways, often involving manual annotations by native speakers. This results in over 14.5k new instances across 6 distinct NLU tasks. Additionally, we present the first results on state-of-the-art monolingual and multilingual pre-trained language models on this benchmark and compare them with human performance, which provides valuable insights into our ability to tackle natural language understanding challenges in Persian. We hope ParsiNLU fosters further research and advances in Persian language understanding.1

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A Study on Classification Techniques based on Opinions

Pooja

Singh

2021

IOP Conf. Ser.: Mater. Sci. Eng.

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Sentiment analysis or opinion mining is one of the considerable undertakings of Natural Language Processing. Sentiment analysis is a quick-growing investigation area in computer science. It includes a way of improvement for the collection and assessment of comments or opinions about legislation, laws, and opinions of products, and so forth, which are posted on the internet. Opinion mining analyzes the opinions given by people. In sentiment analysis, the customer feedback, review, shopping site review, online purchase reviews, politics opinions are considered for the analysis. Sentiment analysis is about detecting the emotions i.e. sad, happy, angry, excited, etc. The main objective of opinion mining is characterizing what the public think and comment. This review paper features certain discussions concerning the opinion-based classification and used approaches for the extraction of feeling assessment and opinion mining. Paper presents a comparison between different classification techniques. Applications and challenges of opinion mining are also discussed in this paper.

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Product Quality Assessment using Opinion Mining in Persian Online Shopping

Cited by 4 publications

References 8 publications

Deep Learning Based Cross Domain Sentiment Classification for Urdu Language

Deep Learning Based Cross Domain Sentiment Classification for Urdu Language

ParsiNLU: A Suite of Language Understanding Challenges for Persian

A Study on Classification Techniques based on Opinions

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