Lexicon-based Non-Compositional Multiword Augmentation Enriching Tweet Sentiment Analysis

Tahayna, Bashar; Ayyasamy, Ramesh Kumar; Akbar, Rehan; Subri, Nor Fatiha Binti; Sangodiah, Anbuselvan

doi:10.1109/aidas56890.2022.9918749

Cited by 6 publications

(5 citation statements)

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“…The study's results will empower tech video content publishers and brand promoters to comprehend viewer feedback and sentiment fluctuations better, optimizing content strategies and communication impacts. Moreover, the findings will furnish reference points for comment section sentiment analysis applications in other domains, broadening the research's scope and depth [8][9][10].…”

Section: Introductionmentioning

confidence: 89%

Identifying positive and negative comments underneath technology videos based on Bert's model

Lu,

Qiu,

Yang

2024

ACE

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With the gradual popularization of the Internet and the increasing richness of content, more and more popular science videos appear in peoples vision. However, people have different attitudes towards them and cannot make explicit judgments. This paper used comments from some popular science videos on Bilibili as a dataset, trained the Bert model using parallel training methods such as (Distributed Data parallel) DDP, and selected different proportions of datasets (20%, 30%, and 40%) for training and testing data to objectively determine the positive and negative attitudes of people in comments. Research has found that different scale datasets have similar performance, with an 86% -87% accuracy rate. Besides, multi-card parallel processing can bring higher returns. The results help people comprehensively understand audience feedback and emotional preferences towards technical content. At the same time, it reveals the complex relationship between the emotions reflected in comments and various factors such as audience engagement, content strategies, and demographics.

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Section: Introductionmentioning

confidence: 89%

Identifying positive and negative comments underneath technology videos based on Bert's model

Lu,

Qiu,

Yang

2024

ACE

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“…They assess the performance of an embedding model that has been fine-tuned for classification. Thangavel et al [38] proposed a lexicon-based method to apply enhanced algorithms for sentiment analysis using tweet data. A lexicon-based technique and framework have been presented for multimodal sentiment analysis of text compiled from audio, pictures, and videos.…”

Section: Sentiment Analysis Using Lexicon-based Methodsmentioning

confidence: 99%

Analysis of customer reviews with an improved VADER lexicon classifier

Barik,

Misra

2024

J Big Data

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Background The importance of customer reviews in determining satisfaction has significantly increased in the digital marketplace. Using sentiment analysis in customer reviews has immense potential but encounters challenges owing to domain heterogeneity. The sentiment orientation of words varies by domain; however, comprehending domain-specific sentiment reviews remains a significant constraint. Aim This study proposes an Improved VADER (IVADER) lexicon-based classification model to evaluate customer sentiment in multiple domains. The model involves constructing a domain-specific dictionary based on the VADER lexicon and classifying doeviews using the constructed dictionary. Methodology The proposed IVADER model uses data preprocessing, Vectorizer transformation, WordnetLemmatizer-based feature selection, and enhanced VADER Lexicon classifier. Result Compared to existing studies, the IVVADER model accomplished outcomes of accuracy of 98.64%, precision of 97%, recall of 94%, f1-measure of 92%, and less training time of 44 s for classification. Outcome Product designers and business organizations can benefit from the IVADER model to evaluate multi-domain customer sentiment and introduce new products in the competitive online marketplace.

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“…The first phase is designed to manage and prepare the idiomatic lexicon. Unlike the proposed method in [28], the aim of this research to automatically annotated the idioms in the lexicon and to compare the error rate of the automation process. The second phase involves the data augmentation unit and the connection to the external knowledge bases.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…In previous research [28], we extended the SliDE lexicon by adding 3930 new idiom. The original Sentiment Lexicon of IDiomatic Expressions (SliDE) of 5000 labelled idioms.…”

Section: A Idiomatic Sentiment Lexiconmentioning

confidence: 99%

“…The original Sentiment Lexicon of IDiomatic Expressions (SliDE) of 5000 labelled idioms. We used the eSliDE sentiment lexicon to extract sentiment labels for the 8,930 idioms in that dataset [28]. The sentiment labels were assigned by the lexicon based on a majority vote of at least 10 crowdsourced annotations for each sentence.…”

Section: A Idiomatic Sentiment Lexiconmentioning

confidence: 99%

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Automatic Sentiment Annotation of Idiomatic Expressions for Sentiment Analysis Task

2022

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Users of social media may use words and phrases literally to convey their views or opinion clearly. However, some people choose to utilise idioms or proverbs that are implicit and indirect in order to make a stronger impression on the audience or perhaps to catch their attention by utilising a funny, sarcastic, or metaphorical phrases. Idioms and proverbs are examples of figurative expressions with a thematically coherent totality that cannot be understood literally. In a previous work, the extension of IBM's Sentiment Lexicon of Idiomatic Expressions was proposed to include around 9,000 idioms; both lexicons are manually annotated by crowdsourcing service. Therefore, in this research, we provide knowledge-based expansion approach to avoid human annotation of idioms. For sentiment classification, the proposed method has the advantage that it does not require any fine-tuning for the BERT model. Experimental comparisons show that the automated idiom enrichment and annotation are very beneficial for the performance of the sentiment classifier. The expanded annotated lexicon will be made available to the general public.

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Lexicon-based Non-Compositional Multiword Augmentation Enriching Tweet Sentiment Analysis

Cited by 6 publications

References 0 publications

Identifying positive and negative comments underneath technology videos based on Bert's model

Identifying positive and negative comments underneath technology videos based on Bert's model

Analysis of customer reviews with an improved VADER lexicon classifier

Automatic Sentiment Annotation of Idiomatic Expressions for Sentiment Analysis Task

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