Aspect-Based Sentiment Analysis Using Deep Learning Convolutional Neural Network

Kumar, Ravindra; Garg, Saloni

doi:10.1007/978-981-13-7166-0_5

Cited by 7 publications

(9 citation statements)

References 4 publications

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“…However, these techniques do not require heavy labeled dataset and require less computing time. In contrast [30] has used Deep Learning Convolutional Neural Network for aspect based sentiment analysis, but the annotation process is dependent on frequency based technique. Hence, this technique also identifies frequent aspects and CNN (Convolutional Neural Network) has been trained on this annotated dataset, it will also suffer from this problem.…”

Section: Critical Analysis and Limitations Of Abstsmentioning

confidence: 99%

“…Classifies only frequent occurring aspects and ignores the less occurring but important aspects [12,30,38].…”

Section: Critical Analysis and Limitations Of Abstsmentioning

confidence: 99%

“…Apart from this, if an Aspect Based Sentiment Analysis technique is incorporating deep learning approach then it does not mean it will also identify infrequent aspect along with frequent ones. Unless or until dataset annotation is free from frequency based techniques [30]. L 1 points to the limitation that exists in unsupervised [12] or supervised [38] or even in deep learning if the annotation is based on frequency based techniques [30].…”

Section: Critical Analysis and Limitations Of Abstsmentioning

confidence: 99%

“…Unless or until dataset annotation is free from frequency based techniques [30]. L 1 points to the limitation that exists in unsupervised [12] or supervised [38] or even in deep learning if the annotation is based on frequency based techniques [30]. An example of L 1 is in product application domain is that the commentator often comments on the aspects such as price, battery life, design and brand or company.…”

Section: Critical Analysis and Limitations Of Abstsmentioning

confidence: 99%

“…Contrastingly, the service and movie 1 https://spacy.io/usage/vectors-similarity application domains both contains NER problem. In addition, none of the state-of-art ABSTs, which have been proposed for service and movie application domains, provide solution for NER problems [7,9,13,30,42,61]. Since, this research is undertaking the investigation and development of ABST for movie application domain, therefore, it is important to handle NER problem (Person Names and Movie Titles) in aspect based sentiment analysis.…”

Section: Critical Analysis and Limitations Of Abstsmentioning

confidence: 99%

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Movie Aspects Identification Model for Aspect Based Sentiment Analysis

Mir

Mahmood

2020

ITC

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Aspect Based Sentiment Analysis techniques have been applied in several application domains. From the last two decades, these techniques have been developed mostly for product and service application domains. However, very few aspect-based sentiment techniques have been proposed for the movie application domain. Moreover, these techniques only mine specific aspects (Script, Director, and Actor) of a movie application domain, nevertheless, the movie application domain is more complex than the product and service application domain. Since, it contains NER (Named Entity Recognition) problem and it cannot be ignored, since there is an opinion often associated with it. Consequently, in this paper MAIM (Movie Aspect Identification Model) is proposed that can extract not only movie specific aspects, also identifies NEs (Named Entities) such as Person Name and Movie Title. The three main contributions are 1) the identification of infrequent aspects, 2) the identification of NE (named entity) in movie application domain, 3) identifying N-gram opinion words as an entity. MAIM incorporates the BiLSTM-CRF hybrid technique and is implemented on the movie application domain having precision 89.9%, recall 88.9% and f1-measure 89.4%. The experimental results show that MAIM performs better than baseline models CRF and LSTM-CRF.

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Section: Critical Analysis and Limitations Of Abstsmentioning

confidence: 99%

“…Classifies only frequent occurring aspects and ignores the less occurring but important aspects [12,30,38].…”

Section: Critical Analysis and Limitations Of Abstsmentioning

confidence: 99%

Section: Critical Analysis and Limitations Of Abstsmentioning

confidence: 99%

Section: Critical Analysis and Limitations Of Abstsmentioning

confidence: 99%

Section: Critical Analysis and Limitations Of Abstsmentioning

confidence: 99%

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Movie Aspects Identification Model for Aspect Based Sentiment Analysis

Mir

Mahmood

2020

ITC

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Systematic reviews in sentiment analysis: a tertiary study

2021

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With advanced digitalisation, we can observe a massive increase of user-generated content on the web that provides opinions of people on different subjects. Sentiment analysis is the computational study of analysing people's feelings and opinions for an entity. The field of sentiment analysis has been the topic of extensive research in the past decades. In this paper, we present the results of a tertiary study, which aims to investigate the current state of the research in this field by synthesizing the results of published secondary studies (i.e., systematic literature review and systematic mapping study) on sentiment analysis. This tertiary study follows the guidelines of systematic literature reviews (SLR) and covers only secondary studies. The outcome of this tertiary study provides a comprehensive overview of the key topics and the different approaches for a variety of tasks in sentiment analysis. Different features, algorithms, and datasets used in sentiment analysis models are mapped. Challenges and open problems are identified that can help to identify points that require research efforts in sentiment analysis. In addition to the tertiary study, we also identified recent 112 deep learning-based sentiment analysis papers and categorized them based on the applied deep learning algorithms. According to this analysis, LSTM and CNN algorithms are the most used deep learning algorithms for sentiment analysis.

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Aspect-Based Approaches for Measuring Customer Feedback in the E-Commerce Industry

Dogra,

2024

2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

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Aspect-Based Sentiment Analysis Using Deep Learning Convolutional Neural Network

Cited by 7 publications

References 4 publications

Movie Aspects Identification Model for Aspect Based Sentiment Analysis

Movie Aspects Identification Model for Aspect Based Sentiment Analysis

Systematic reviews in sentiment analysis: a tertiary study

Aspect-Based Approaches for Measuring Customer Feedback in the E-Commerce Industry

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