Corpus-Based Techniques for Sentiment Lexicon Generation: A Review

Darwich, Mohammad; Noah, Shahrul Azman Mohd; Omar, Nazlia; Osman, Nurul Aida

doi:10.6025/jdim/2019/17/5/296-305

Cited by 30 publications

(14 citation statements)

References 17 publications

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“…Li et al ( 2017 ) extended their work, proposing a class of linear regression models trained with word embeddings to predict affective meanings in several sentiment lexicons such as ANEW (Bradley and Lang, 1999 ), VAD (Mohammad, 2018 ). Darwich et al ( 2019 ) present an excellent review of ASLG.…”

Section: Related Workmentioning

confidence: 99%

Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning

Alshaabi

Oort

Fudolig

et al. 2022

Front. Artif. Intell.

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Sentiment-aware intelligent systems are essential to a wide array of applications. These systems are driven by language models which broadly fall into two paradigms: Lexicon-based and contextual. Although recent contextual models are increasingly dominant, we still see demand for lexicon-based models because of their interpretability and ease of use. For example, lexicon-based models allow researchers to readily determine which words and phrases contribute most to a change in measured sentiment. A challenge for any lexicon-based approach is that the lexicon needs to be routinely expanded with new words and expressions. Here, we propose two models for automatic lexicon expansion. Our first model establishes a baseline employing a simple and shallow neural network initialized with pre-trained word embeddings using a non-contextual approach. Our second model improves upon our baseline, featuring a deep Transformer-based network that brings to bear word definitions to estimate their lexical polarity. Our evaluation shows that both models are able to score new words with a similar accuracy to reviewers from Amazon Mechanical Turk, but at a fraction of the cost.

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Section: Related Workmentioning

confidence: 99%

Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning

Alshaabi

Oort

Fudolig

et al. 2022

Front. Artif. Intell.

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“…Dictionary-based approach judges the sentiments based on terms available in the lexicons and uses the online dictionaries to tag these words. On the contrary, the corpus-based approach relies on co-occurrences of syntactic or statistic patterns embedded in text corpora and a predefine set of positive and negative seed words [ 19 , 20 ]. Semantic-Oriented CALculator (SO-CAL) calculates the semantic orientation of the words from the applied dictionary and predicts the intensification and negation of the phrase.…”

Section: Literature Reviewmentioning

confidence: 99%

KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment Analysis

Tiwari

Nagpal

2022

New Gener. Comput.

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Social media materialized as an influential platform that allows people to share their views on global and local issues. Sentiment analysis can handle these massive amounts of unstructured reviews and convert them into meaningful opinions. Undoubtedly, COVID-19 originated as the enormous challenge across the world that physically and financially bruted humankind. Meanwhile, farmers' protests shook up the world against three pieces of legislation passed by the Indian government. Hence, an artificial intelligence-based sentiment model is needed for suggesting the right direction toward outbreaks. Although Deep Neural Network (DNN) gained popularity in sentiment analysis applications, these still have a limitation of sequential training, high-dimension feature space, and equal feature importance distribution. In addition, inaccurate polarity scoring and utility-based topic modeling are other challenging aspects of sentiment analysis. It motivates us to propose a Knowledge-Enriched Attention-based Hybrid Transformer (KEAHT) model by enriching the explicit knowledge of Latent Dirichlet Allocation (LDA) topic modeling and lexicalized domain ontology. A pre-trained Bidirectional Encoder Representation from Transformer (BERT) is employed to train within a minimum training corpus. It provides the facility of attention mechanism and can solve complex text problems accurately. A comparative study with existing baselines and recent hybrid models affirms the credibility of the proposed KEAHT in the field of Natural Language Processing (NLP). This model emphasizes artificial intelligence's role in handling the situation of the global pandemic and democratic dispute in a country. Furthermore, two benchmark datasets, namely “COVID-19-Vaccine-Labelled-Tweets" and "Indian-Farmer-Protest-Labelled-Tweets”, are also constructed to accommodate future researchers for outlining the essential facts associated with the outbreaks.

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“…The lexicon-based sentiment analysis methods are widely classified into two approaches: (i) dictionary based and (ii) corpus based [19]. In dictionary-based approach, the words with their polarity scores are utilized for sentiment analysis.…”

Section: Hybrid Sentiment Analysismentioning

confidence: 99%

A Novel Sentiment Analysis for Amazon Data with TSA based Feature Selection

Daniel

Meena

2021

SCPE

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Sentiment analysis of online product reviews has become a mainstream way for businesses on e-commerce platforms to promote their products and improve user satisfaction. Hence, it is necessary to construct an automatic sentiment analyser for automatic identification of sentiment polarity of the online product reviews. Traditional lexicon-based approaches used for sentiment analysis suffered from several accuracy issues while machine learning techniques require labelled training data. This paper introduces a hybrid sentiment analysis framework to bond the gap between both machine learning and lexicon-based approaches. A novel tunicate swarm algorithm (TSA) based feature reduction is integrated with the proposed hybrid method to solve the scalability issue that arises due to a large feature set. It reduces the feature set size to 43% without changing the accuracy (93%). Besides, it improves the scalability, reduces the computation time and enhances the overall performance of the proposed framework. From experimental analysis, it can be observed that TSA outperforms existing feature selection techniques such as particle swarm optimization and genetic algorithm. Moreover, the proposed approach is analysed with performance metrics such as recall, precision, F1-score, feature size and computation time.

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Corpus-Based Techniques for Sentiment Lexicon Generation: A Review

Cited by 30 publications

References 17 publications

Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning

Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning

KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment Analysis

A Novel Sentiment Analysis for Amazon Data with TSA based Feature Selection

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