Implicit sentiment analysis based on graph attention neural network

Yang, Shanliang; Xing, Linlin; Li, Yongming; Chang, Zheng

doi:10.1002/eng2.12452

Cited by 17 publications

(10 citation statements)

References 32 publications

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“…Neural tensor networks can be used for knowledge base completion, inferring relationships that were missed during construction (Socher et al, 2013a ). Graph neural networks can create rich features from the relationships captured in knowledge bases, allowing sentiment analysis models to handle complex context-based problems (Dowlagar and Mamidi, 2021 ; Liao et al, 2021 ; Yang et al, 2021 ). Ensembles of symbolic and sub-symbolic AI can be used to cover the individual weaknesses of each method (Cambria et al, 2020 ).…”

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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“…This process can thus be automated using sentiment analysis tools. 5 Sentiment analysis is a discipline that uses machine learning and natural language processing (NLP) to determine what a certain group of people feel about an issue or product. 5 It has been applied in business intelligence to understand the subjective reasons why consumers are or are not responding to something.…”

Section: Introductionmentioning

confidence: 99%

“…5 Sentiment analysis is a discipline that uses machine learning and natural language processing (NLP) to determine what a certain group of people feel about an issue or product. 5 It has been applied in business intelligence to understand the subjective reasons why consumers are or are not responding to something. For instance, the reasons why consumers buy a product in particular, what the customers think of the user experience for the products or services they have used and whether the customer service support met their expectations.…”

Section: Introductionmentioning

confidence: 99%

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Sentiment analysis on social media tweets using dimensionality reduction and natural language processing

Omuya

Okeyo

Kimwele

2022

Engineering Reports

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Social media has been embraced by different people as a convenient and official medium of communication. People write or share messages and attach images and videos on Twitter, Facebook and other social media platforms. It therefore generates a lot of data that is rich in sentiments. Sentiment analysis has been used to determine the opinions of clients, for instance, relating to a particular product or company. Lexicon and machine learning approaches are the strategies that have been used to analyze these sentiments. The performance of sentiment analysis is, however, distorted by noise, the curse of dimensionality, the data domains and the size of data used for training and testing. This article aims at developing a model for sentiment analysis of social media data in which dimensionality reduction and natural language processing with part of speech tagging are incorporated. The model is tested using Naïve Bayes, support vector machine, and K‐nearest neighbor algorithms, and its performance compared with that of two other sentiment analysis models. Experimental results show that the model improves sentiment analysis performance using machine learning techniques.

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“…Named-entity recognition (NER) [1][2][3][4][5][6] aims to mark the boundaries and categories of entity names in a chunk of text. It is a fundamental task in natural language processing (NLP) and plays a critical role in many downstream tasks, including sentiment analysis, [7][8][9][10] entity linking, 11 relation extraction, 12 knowledge graph, 13 and question answering. 14 NER has long been the focus of attention in academia and industry.…”

Section: Introductionmentioning

confidence: 99%

Deep adaptation ofCNNin Chinese named entity recognition

Qin

et al. 2022

Engineering Reports

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Named entity recognition (NER) is an important task in the field of natural language processing, but it is more challenging in Chinese because of the lack of natural delimiters. The traditional character‐based Chinese NER model directly uses long short‐term memory (LSTM), gated recurrent units, and other sequence models to extract sentence‐level information from character sequences, resulting in the lack of word‐level information in the model. Therefore, a Chinese NER model called ChineseBERT‐CNNs‐BiLSTM‐CRF was proposed, which uses the ChineseBERT pretrained model as the embedding layer so that the vector representation of each Chinese character contained pinyin, glyph, and conventional character information. In addition, a CNN‐based neural network structure called CNNs was presented to extract word‐level information from character sequences and alleviate the problem of entity boundary recognition. BiLSTM was used to extract global features (i.e., sentence‐level information) and predict the corresponding labels of character sequences. Further, conditional random field (CRF) was employed to impose certain rules on the prediction of BiLSTM to enhance the recognition effect of the model. The experimental results revealed that the F1 values of the model on MSRA, people's Daily, and Weibo datasets reached 95.76, 96.61, and 70.00%, respectively, highlighting the effectiveness of the model.

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Implicit sentiment analysis based on graph attention neural network

Cited by 17 publications

References 32 publications

Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning

Augmenting Semantic Lexicons Using Word Embeddings and Transfer Learning

Sentiment analysis on social media tweets using dimensionality reduction and natural language processing

Deep adaptation ofCNNin Chinese named entity recognition

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