Sentic Parser: A Graph-Based Approach to Concept Extraction for Sentiment Analysis

Cambria, Erik; Mao, Rui; Han, Sooji; Liu, Qian

doi:10.1109/icdmw58026.2022.00060

Cited by 19 publications

(4 citation statements)

References 46 publications

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“…Future work will focus on incorporating other useful external knowledge to improve graph propagation and consider long-range information between word pairs to extend the adjacent inference strategy. Another direction is to investigate recent advancements in ABSA tasks such as large language models (LLMs) [55], prompt-based methods [56] and neurosymbolic AI frameworks [57] to improve ASTE.…”

Section: Discussionmentioning

confidence: 99%

Encoding Syntactic Information into Transformers for Aspect-Based Sentiment Triplet Extraction

Wang

et al. 2024

IEEE Trans. Affective Comput.

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Aspect-based sentiment triplet extraction (ASTE) aims to extract triplets consisting of aspect terms and their associated opinion terms and sentiment polarities from sentences, a relatively new and challenging subtask of aspect-based sentiment analysis (ABSA). Previous studies have used either pipeline models or unified tagging schema models. These models ignore the syntactic relationships between the aspect and its corresponding opinion words, which leads them to mistakenly focus on syntactically unrelated words. One feasible option is to use a graph convolution network (GCN) to exploit syntactic information by propagating the representation from the opinion words to the aspect. However, such a method considers all syntactic dependencies to be of the same type and thus may still incorrectly associate unrelated words to the target aspect through the iterations of graph convolutional propagation. Herein, a syntax-aware transformer (SA-Transformer) is proposed to extend the GCN strategy by fully exploiting the dependency types of edges to block inappropriate propagation. The proposed approach can obtain different representations and weights even for edges with the same dependency type according to their adjacent dependency type of edges. Instead of using a GCN layer, we used an L-layer SA transformer to encode syntactic information in the word-pair representation to improve performance. Experimental results on four benchmark datasets show that the proposed model outperforms various previous models for ASTE.

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Section: Discussionmentioning

confidence: 99%

Encoding Syntactic Information into Transformers for Aspect-Based Sentiment Triplet Extraction

Wang

et al. 2024

IEEE Trans. Affective Comput.

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“…On the one hand, it is the preferred learning type for Sentiment Analysis (Hemalatha et al 2013). On the other hand, because of the complexity of this theme, which obligates better support on the algorithm to identify the sentiment in Social Media text, usually filled with irony and sarcasm, a figure of speech that current sentiment analysis techniques struggle to capture (Cambria et al 2022). Sarcasm is not the only challenge in sentiment analysis.…”

Section: Supervised Learningmentioning

confidence: 99%

“…Sarcasm is not the only challenge in sentiment analysis. Other challenges include polarity disambiguation (Cambria et al 2022), filtering neutral opinions and ambivalent opinions (opinions with both positive and negative sentiments) since these opinions can influence the overall perception of the sentiment (Chan et al 2023;Rahmani et al 2023). These issues have been addressed by new frameworks and techniques primarily based on neural networks and graph architectures (Cambria et al 2022;Chan et al 2023;Dai et al 2021;Rahmani et al 2023).…”

Section: Supervised Learningmentioning

confidence: 99%

Social media discourse and voting decisions influence: sentiment analysis in tweets during an electoral period

Rita

António

Afonso

2023

Soc. Netw. Anal. Min.

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In a time where social media is fundamental for any political campaign and to share a message with an electoral audience, this study searches for a conclusion of the actual persuasion capacity of social media in the electors when they need to decide whom to vote for as their next government. For this, it compares the sentiment that Social Media users demonstrated during an electoral period with the actual results of those elections. For this analysis, it was used, as a case study, tweets mentioning the two major English parties, Conservative and Labor, their respective candidates for the position of prime minister, and terms that identified their political campaign during the electoral period of the General Elections of the United Kingdom that occurred on December 12, 2019. Data were collected using R. The treatment and analysis were done with R and RapidMiner. Results show that tweets’ sentiment is not a reliable election results predictor. Additionally, results also show that it is impossible to state that social media impacts voting decisions. At least not from the polarity of the sentiment of opinions on social media.

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“…Additionally, Cui et al [91] introduced another label propagation method based on the extraction and analysis of emotion tokens. Recently, a graph-based technique was presented by Cambria et al [92] where reasoning tasks were performed by developing a morphologyaware concept parser. Since construction of the social graph is time-consuming, and the availability of the graph is greatly dependent on the diversity of the corpus, this area of study requires further investigation.…”

Section: Other Approachesmentioning

confidence: 99%

Sentiment Analysis of Twitter Data

et al. 2022

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Twitter has become a major social media platform and has attracted considerable interest among researchers in sentiment analysis. Research into Twitter Sentiment Analysis (TSA) is an active subfield of text mining. TSA refers to the use of computers to process the subjective nature of Twitter data, including its opinions and sentiments. In this research, a thorough review of the most recent developments in this area, and a wide range of newly proposed algorithms and applications are explored. Each publication is arranged into a category based on its significance to a particular type of TSA method. The purpose of this survey is to provide a concise, nearly comprehensive overview of TSA techniques and related fields. The primary contributions of the survey are the detailed classifications of numerous recent articles and the depiction of the current direction of research in the field of TSA.

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Sentic Parser: A Graph-Based Approach to Concept Extraction for Sentiment Analysis

Cited by 19 publications

References 46 publications

Encoding Syntactic Information into Transformers for Aspect-Based Sentiment Triplet Extraction

Encoding Syntactic Information into Transformers for Aspect-Based Sentiment Triplet Extraction

Social media discourse and voting decisions influence: sentiment analysis in tweets during an electoral period

Sentiment Analysis of Twitter Data

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