Movie Aspects Identification Model for Aspect Based Sentiment Analysis

Mir, Jibran; Mahmood, Azhar

doi:10.5755/j01.itc.49.4.25350

Cited by 4 publications

(3 citation statements)

References 52 publications

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“…where Ws is weight matrix and bs is bias matrix. 𝛼𝛼𝛼𝛼 𝑖𝑖𝑖𝑖 improves the explanatory ability of the model and it helps us in drawing out the high sentiment score word [17]. Attention vector also eases the cross domain transfer learning.…”

Section: Sentence Representationmentioning

confidence: 99%

Improved Feature Representation Using Collaborative Network for Cross-Domain Sentiment Analysis

Gunasekar¹,

Thilagamani²

2023

ITC

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Sentiment Analysis task helps us to estimate the opinion of a person from his reviews or comments about a product, person, politics, etc., Cross-Domain Sentiment Analysis (CDSA) empowers the Sentiment models with the ability to forecast the opinion of a review coming from a different domain other than the domain where the model is trained. The challenge of the CDSA model relies on bridging the relationship between words in the source and target domain. Several types of research in CDSA focus on determining the domain invariant features to adapt the model to the target domain, such model shows less focus on aspect terms of the sentence. We propose CWAN (Collaborative Word Attention Network), which integrates aspects and domain invariant features of the sentences to calculate the sentiment. CWAN uses attention networks to capture the domain-independent features and aspects of the sentences. The sentence and aspect attention models are executed collaboratively to determine the sentiment of the sentence. Amazon product review dataset is used in this experiment. The performance of the CWAN model is compared with other baseline CDSA models. The results show that CWAN outperforms other baseline models.

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Section: Sentence Representationmentioning

confidence: 99%

Improved Feature Representation Using Collaborative Network for Cross-Domain Sentiment Analysis

Gunasekar¹,

Thilagamani²

2023

ITC

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“…An end-to-end architecture of the proposed approach is shown in Fig. 1, which consists of annotation, explicit aspects identification [40], and implicit aspects mapping phases. In the following section, the techniques used in each stage are described.…”

Section: Proposed Approach For Mapping Implicit Aspects To Explicit Aspectsmentioning

confidence: 99%

Multi-Level Knowledge Engineering Approach for Mapping Implicit Aspects to Explicit Aspects

Mir¹,

Mahmood²,

Khatoon³

2022

Computers, Materials &Amp; Continua

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Aspect's extraction is a critical task in aspect-based sentiment analysis, including explicit and implicit aspects identification. While extensive research has identified explicit aspects, little effort has been put forward on implicit aspects extraction due to the complexity of the problem. Moreover, existing research on implicit aspect identification is widely carried out on product reviews targeting specific aspects while neglecting sentences' dependency problems. Therefore, in this paper, a multi-level knowledge engineering approach for identifying implicit movie aspects is proposed. The proposed method first identifies explicit aspects using a variant of BiLSTM and CRF (Bidirectional Long Short Memory-Conditional Random Field), which serve as a memory to process dependent sentences to infer implicit aspects. It can identify implicit aspects from four types of sentences, including independent and three types of dependent sentences. The study is evaluated on a large movie reviews dataset with 50k examples. The experimental results showed that the explicit aspect identification method achieved 89% F1-score and implicit aspect extraction methods achieved 76% F1-score. In addition, the proposed approach also performs better than the state-of-the-art techniques (NMFIAD and ML-KB+) on the product review dataset, where it achieved 93% precision, 92% recall, and 93% F1-score.

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“…Many deep learning approaches for ABSA tasks have been developed in recent years that are more scalable than classic feature-based methods [10][11][12][13][14][15]. BERT [16] uses semantic combination functions to handle sentiment analysis's complicated combinatorial nature. To increase prediction accuracy, BERT models encode sentence sequence information, obtain remote dependencies and build representations of phrases.…”

Section: Introductionmentioning

confidence: 99%

Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

Rana¹,

Rehman²,

Nawaz³

et al. 2023

Computer Systems Science and Engineering

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People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events, public products and the latest affairs. People share their thoughts and feelings about various topics, including products, news, blogs, etc. In user reviews and tweets, sentiment analysis is used to discover opinions and feelings. Sentiment polarity is a term used to describe how sentiment is represented. Positive, neutral and negative are all examples of it. This area is still in its infancy and needs several critical upgrades. Slang and hidden emotions can detract from the accuracy of traditional techniques. Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories. Some existing strategies are domain-specific. The proposed model incorporates aspect extraction, association rule mining and the deep learning technique Bidirectional Encoder Representations from Transformers (BERT). Aspects are extracted using Part of Speech Tagger and association rule mining is used to associate aspects with opinion words. Later, classification was performed using BER. The proposed approach attained an average of 89.45% accuracy, 88.45% precision and 85.98% recall on different datasets of products and Twitter. The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

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Movie Aspects Identification Model for Aspect Based Sentiment Analysis

Cited by 4 publications

References 52 publications

Improved Feature Representation Using Collaborative Network for Cross-Domain Sentiment Analysis

Improved Feature Representation Using Collaborative Network for Cross-Domain Sentiment Analysis

Multi-Level Knowledge Engineering Approach for Mapping Implicit Aspects to Explicit Aspects

Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

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