SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis

Wang, Zhaoxia; Li, Yang; Xing, Frank Z.; Poria, Soujanya; Kwok, Kenneth

doi:10.1145/3340531.3412003

Cited by 335 publications

(155 citation statements)

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“…by integrating significant variations in the concept collections and domain distribution of occurrences and co-occurrences linked to future releases of the domain corpora and external knowledge bases; and 2. by including timestamps (e.g., campaign start times) of the domain corpora (e.g., dumps of Kickstarter campaign URLs 9 ) or other references to specific time in a temporal dimension in domain distributional information. 8 Real-time data are widely recognized as the life blood of a variety of applications (e.g., [10]) 13 OKR models are commonly used by very successful companies such as Amazon, Facebook, and Google. https ://www.whatm atter s. com/faqs/how-to-grade -okrs https ://conce ptboa rd.com/blog/okrgoogl e-goal-setti ng-succe ss/ -It leverages DomainSenticNet to further tune (for a given domain of interest) the OKR scale for the interpretations of the emotional intensities.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, other possible future research might aim at "propagating" the Hourglass of Emotions dimension weights and polarities to a collection of added external concepts. In addition, similar to [8,11], our resource opens an avenue for further research on the generation of contextual domain embeddings in deep neural network-based applications. Finally, as discussed in Section 5, approaches such as [1,21,22] can leverage DomainSenticNet as an effective resource to improve the interpretability and explainability of domain-aware sentic applications.…”

Section: Discussionmentioning

confidence: 99%

“…The presented approach leveraged the titles and abstracts of 600 domain publications and complemented a given ontology with additional concepts and axioms by means of a phrase-based 16 A recent model revision is described in [35] 15 SenticNet 6 has recently been released. This updated resource now contains 200K concepts [8] topic model approach. In a similar direction, the authors of [39] proposed the addition of SOBA-a semiautomated methodology to generate ontologies-to ODSA applications.…”

Section: Related Workmentioning

confidence: 99%

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DomainSenticNet: An Ontology and a Methodology Enabling Domain-Aware Sentic Computing

et al. 2021

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In recent years, SenticNet and OntoSenticNet have represented important developments in the novel interdisciplinary field of research known as sentic computing, enabling the development of a variety of Sentic applications. In this paper, we propose an extension of the OntoSenticNet ontology, named DomainSenticNet, and contribute an unsupervised methodology to support the development of domain-aware Sentic applications. We developed an unsupervised methodology that, for each concept in OntoSenticNet, mines semantically related concepts from WordNet and Probase knowledge bases and computes domain distributional information from the entire collection of Kickstarter domain-specific crowdfunding campaigns. Subsequently, we applied DomainSenticNet to a prototype tool for Kickstarter campaign authoring and success prediction, demonstrating an improvement in the interpretability of sentiment intensities. DomainSenticNet is an extension of the OntoSenticNet ontology that integrates each of the 100,000 concepts included in OntoSenticNet with a set of semantically related concepts and domain distributional information. The defined unsupervised methodology is highly replicable and can be easily adapted to build similar domain-aware resources from different domain corpora and external knowledge bases. Used in combination with OntoSenticNet, DomainSenticNet may favor the development of novel hybrid aspect-based sentiment analysis systems and support further research on sentic computing in domain-aware applications.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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DomainSenticNet: An Ontology and a Methodology Enabling Domain-Aware Sentic Computing

et al. 2021

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“…We used the following lexical items to determine the sentiment of a text: Bing Liu [26], Sentiment 140 [48], NRC [49], Affin [50] and SenticNet [51]. Each of these lexicons has-as part of their structure-the elements word and value.…”

Section: Lexicon-based Analysismentioning

confidence: 99%

Does Twitter Affect Stock Market Decisions? Financial Sentiment Analysis During Pandemics: A Comparative Study of the H1N1 and the COVID-19 Periods

et al. 2021

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Investors are constantly aware of the behaviour of stock markets. This affects their emotions and motivates them to buy or sell shares. Financial sentiment analysis allows us to understand the effect of social media reactions and emotions on the stock market and vice versa. In this research, we analyse Twitter data and important worldwide financial indices to answer the following question: How does the polarity generated by Twitter posts influence the behaviour of financial indices during pandemics? This study is based on the financial sentiment analysis of influential Twitter accounts and its relationship with the behaviour of important financial indices. To carry out this analysis, we used fundamental and technical financial analysis combined with a lexicon-based approach on financial Twitter accounts. We calculated the correlations between the polarities of financial market indicators and posts on Twitter by applying a date shift on tweets. In addition, correlations were identified days before and after the existing posts on financial Twitter accounts. Our findings show that the markets reacted 0 to 10 days after the information was shared and disseminated on Twitter during the COVID-19 pandemic and 0 to 15 days after the information was shared and disseminated on Twitter during the H1N1 pandemic. We identified an inverse relationship: Twitter accounts presented reactions to financial market behaviour within a period of 0 to 11 days during the H1N1 pandemic and 0 to 6 days during the COVID-19 pandemic. We also found that our method is better at detecting highly shifted correlations by using SenticNet compared with other lexicons. With SenticNet, it is possible to detect correlations even on the same day as the Twitter posts. The most influential Twitter accounts during the period of the pandemic were The New York Times, Bloomberg, CNN News and Investing.com, presenting a very high correlation between sentiments on Twitter and stock market behaviour. The combination of a lexicon-based approach is enhanced by a shifted correlation analysis, as latent or hidden correlations can be found in data.

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“…In addition, we assign a negative sentiment strength and a sentiment strength of −1 to the words in the negative lexicon. -SenticNet 6.0 Lexicon -Cambria et al [45] introduced an approach that combines both symbolic and subsymbolic models and leverages their strengths. In this research, we make use of the sixth version of the SenticNet knowledge base.…”

Section: -Loughran and Mcdonald Lexicons (Lm) -Loughran Andmentioning

confidence: 99%

Automatically Building Financial Sentiment Lexicons While Accounting for Negation

Bos

Frăsincar

2021

Cogn Comput

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Financial investors make trades based on available information. Previous research has proved that microblogs are a useful source for supporting stock market decisions. However, the financial domain lacks specific sentiment lexicons that could be utilized to extract the sentiment from these microblogs. In this research, we investigate automatic approaches that can be used to build financial sentiment lexicons. We introduce weighted versions of the Pointwise Mutual Information approaches to build sentiment lexicons automatically. Furthermore, existing sentiment lexicons often neglect negation while building the sentiment lexicons. In this research, we also propose two methods (Negated Word and Flip Sentiment) to extend the sentiment building approaches to take into account negation when constructing a sentiment lexicon. We build the financial sentiment lexicons by leveraging 200,000 messages from StockTwits. We evaluate the constructed financial sentiment lexicons in two different sentiment classification tasks (unsupervised and supervised). In addition, the created financial sentiment lexicons are compared with each other and with other existing sentiment lexicons. The best performing financial sentiment lexicon is built by combining our Weighted Normalized Pointwise Mutual Information approach with the Negated Word approach. It outperforms all the other sentiment lexicons in the two sentiment classification tasks. In the unsupervised sentiment classification task, it has, on average, a balanced accuracy of 69.4%, and in the supervised setting, a balanced accuracy of 75.1%. Moreover, the various sentiment classification tasks confirm that the sentiment lexicons could be improved by taking into account negation while building the sentiment lexicons. The improvement could be made by using one of the proposed methods to incorporate negation in the sentiment lexicon construction process.

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SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis

Cited by 335 publications

References 46 publications

DomainSenticNet: An Ontology and a Methodology Enabling Domain-Aware Sentic Computing

DomainSenticNet: An Ontology and a Methodology Enabling Domain-Aware Sentic Computing

Does Twitter Affect Stock Market Decisions? Financial Sentiment Analysis During Pandemics: A Comparative Study of the H1N1 and the COVID-19 Periods

Automatically Building Financial Sentiment Lexicons While Accounting for Negation

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