Comparative Study on Language Rule Based Methods for Aspect Extraction in Sentiment Analysis

Ruskanda, Fariska Zakhralativa; Widyantoro, Dwi H.; Purwarianti, Ayu

doi:10.1109/ialp.2018.8629140

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…These are laborious, complicated, and time-consuming approaches that require a lot of effort [22,[27][28][29][30]. In the early days of ABSA, machine learning methods, such as semi-supervised [31,32], supervised [33,34], and unsupervised [35], lexicons-based approaches (i.e., domain-based lexicons and SentiWordNets) [36][37][38][39][40], rule-based or pattern-based techniques [41][42][43][44][45][46][47][48], topic modelling based procedures [49][50][51], and tree or graph-based strategies [52,53] accomplished the feature and sentiment extraction task. Today, DL methods are famous for ABSA tasks, but including reasoning like the human brain remains an open research area.…”

Section: Related Workmentioning

confidence: 99%

Aspect-Based Sentiment Classification Using Deep Learning and Hybrid of Word Embedding and Contextual Position

Ahmad,

Ullah Khan,

Khaled Alarfaj

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Section: Related Workmentioning

confidence: 99%

Aspect-Based Sentiment Classification Using Deep Learning and Hybrid of Word Embedding and Contextual Position

Ahmad,

Ullah Khan,

Khaled Alarfaj

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…These are complicated, laborious, and time-consuming techniques which demand a lot of effort from analysts [12], [14], [23], [24], [25]. Moreover, the feature extraction task accomplishes through machine learning approaches such as supervised [26], [27], semi-supervised [28], [29], and unsupervised [30], lexicons-based methods, e.g., SentiWordNet and domain-based lexicons [2], [31], [32], [33], [34], rulebased or pattern-based approaches [7], [35], [36], [37], [38], [39], [40], [41], topic modelling based techniques [42], [43], comprising MC-CNN that merges three vector representations to perform the task of ABSA. These incorporate GloVe, word2vec, and a one-hot character-based embedding.…”

Section: Related Workmentioning

confidence: 99%

Attention-Based Multi-Channel Gated Recurrent Neural Networks: A Novel Feature-Centric Approach for Aspect-Based Sentiment Classification

et al. 2023

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Sentiment analysis is an active research domain of the current era, thanks to its vast applications. The main objective is to classify the polarity of the given text as negative, positive, or neutral. Thus, researchers' focus shifted towards the aspect or feature-based sentiment analysis because overall polarity does not determine the people's views towards certain features. Therefore, Aspect-Based Sentiment Analysis (ABSA) helps us to identify the sentiments about various aspects of different services and products. However, their accurate identification and extraction are still challenging for the research community due to the complex nature of natural languages. This paper presents a method named Attention-based Multi-Channel Gated Recurrent Neural Network (Att-MC-GRU), which extracts aspects and analyzes textual reviews to predict or classify their sentiments. It introduces the hybrid approach by combining word embedding, part of speech (POS) tags, and contextual position information. The main novelty lies in proposal of a Multi-Channel Gated Recurrent Neural Network (MC-GRU), in contrast to the existing studies that consider Recurrent Neural Networks (RNN) comprising only a single input channel. In addition, word embedding, POS tags, and contextual position information collectively improve the identification and prediction accuracy of aspects and their associated sentiments. Due to the application of the filtering by the attention mechanism that figured out first the significant words, which helps to determine entities' aspects related to the sentiment expressed. The empirical analysis proves the proposed approach's effectiveness compared to the existing techniques in the relevant literature using standard datasets. According to the empirical analysis, the proposed model performs better in the F1-measure, with an overall achievement of 94% in the task of aspect extraction and 93% in the classification of sentiment.INDEX TERMS Attention mechanism, aspect extraction, contextual position information, deep learning multi-channel, gated recurrent unit, part of speech, sentiment analysis, word embedding.

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“…Moreover, the precise identification and extraction of these features still demand attention from the research community. Traditionally, these extractions and identifications of features accomplish through various existing methodologies, such as machine learning [5][6][7]; topic modeling [8][9][10]; and lexicon-based [11][12][13], rule-based, and syntactic relation-based [14][15][16][17][18] methods. Syntactic pattern techniques perform well while extracting features and classifying their sentiments.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Multichannel-Based Deep Models Using Deep Features for Feature-Oriented Sentiment Analysis

et al. 2023

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With the rapid growth of user-generated content on social media, several new research domains have emerged, and sentiment analysis (SA) is one of the active research areas due to its significance. In the field of feature-oriented sentiment analysis, both convolutional neural network (CNN) and gated recurrent unit (GRU) performed well. The former is widely used for local feature extraction, whereas the latter is suitable for extracting global contextual information or long-term dependencies. In existing studies, the focus has been to combine them as a single framework; however, these approaches fail to fairly distribute the features as inputs, such as word embedding, part-of-speech (PoS) tags, dependency relations, and contextual position information. To solve this issue, in this manuscript, we propose a technique that combines variant algorithms in a parallel manner and treats them equally to extract advantageous informative features, usually known as aspects, and then performs sentiment classification. Thus, the proposed methodology combines a multichannel convolutional neural network (MC-CNN) with a multichannel bidirectional gated recurrent unit (MC-Bi-GRU) and provides them with equal input parameters. In addition, sharing the information of hidden layers between parallelly combined algorithms becomes another cause of achieving the benefits of their combined abilities. These abilities make this approach distinctive and novel compared to the existing methodologies. An extensive empirical analysis carried out on several standard datasets confirms that the proposed technique outperforms the latest existing models.

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Comparative Study on Language Rule Based Methods for Aspect Extraction in Sentiment Analysis

Cited by 8 publications

References 9 publications

Aspect-Based Sentiment Classification Using Deep Learning and Hybrid of Word Embedding and Contextual Position

Aspect-Based Sentiment Classification Using Deep Learning and Hybrid of Word Embedding and Contextual Position

Attention-Based Multi-Channel Gated Recurrent Neural Networks: A Novel Feature-Centric Approach for Aspect-Based Sentiment Classification

Hybrid Multichannel-Based Deep Models Using Deep Features for Feature-Oriented Sentiment Analysis

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