Sentiment analysis based on rhetorical structure theory:Learning deep neural networks from discourse trees

Kraus, Mathias; Feuerriegel, Stefan

doi:10.1016/j.eswa.2018.10.002

Cited by 50 publications

(20 citation statements)

References 39 publications

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“…As anticipated, we investigate the rhetorical structure of the US Presidents by using a different method with respect to the proposals adopting a computer science perspective. In this framework, we mention [24] for the case of web documents summarization, [25] where a deep learning algorithm is used to run a semantic analysis or [26], that deals with a rhetoric study of legal documents. Our work falls in the context of the processes that have to be implemented before the application of machine learning models or automated discourse processing methods (see e.g.…”

Section: Introductionmentioning

confidence: 99%

A joint text mining-rank size investigation of the rhetoric structures of the US Presidents’ speeches

Ficcadenti

Cerqueti

Ausloos

2019

Expert Systems with Applications

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This work presents a text mining context and its use for a deep analysis of the messages delivered by the politicians. Specifically, we deal with an expert systems-based exploration of the rhetoric dynamics of a large collection of US Presidents' speeches, ranging from Washington to Trump. In particular, speeches are viewed as complex expert systems whose structures can be effectively analyzed through rank-size laws. The methodological contribution of the paper is twofold. First, we develop a text mining-based procedure for the construction of the dataset by using a web scraping routine on the Miller Center website -the repository collecting the speeches. Second, we explore the implicit structure of the discourse data by implementing a rank-size procedure over the individual speeches, being the words of each speech ranked in terms of their frequencies. The scientific significance of the proposed combination of text-mining and rank-size approaches can be found in its flexibility and generality, which let it be reproducible to a wide set of expert systems and text mining contexts. The usefulness of the proposed method and the speech subsequent analysis is demonstrated by the findings themselves. Indeed, in terms of impact, it is worth noting that interesting conclusions of social, political and linguistic nature on how 45 United States Presidents, from April 30, 1789 till February 28, 2017 delivered political messages can be carried out. Indeed, the proposed analysis shows some remarkable regularities, not only inside a given speech, but also among different speeches. Moreover, under a purely methodological perspective, the presented contribution suggests possible ways of generating a linguistic decision-making algorithm.

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Section: Introductionmentioning

confidence: 99%

A joint text mining-rank size investigation of the rhetoric structures of the US Presidents’ speeches

Ficcadenti

Cerqueti

Ausloos

2019

Expert Systems with Applications

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“…Their baseline model counts term-frequencies in the document, to produce a document-term-matrix where the term frequencies are scaled using tf-idf -placing stronger weights on characteristic terms. As explained before, and further reiterated in [25], the benefit of this particular, proposed, model allows for clauses to be weighted based on their overall significance to the entire document -finally going on to say, that compared to their chosen, unsophisticated, benchmark, Discourse-LSTM outperforms when distinguishing polarity in text.…”

Section: Structure-based Analysismentioning

confidence: 97%

“…In further contrast with the proposed method whereby, based on the structure of the sentence, we can weigh each span by how relevant it is to the final polarity of the document. The authors of [25] also appropriate the study of Discourse Structures in their study where they intend to teach Deep Neural Networks from Discourse Trees to develop a discourse-aware method for sentiment analysis "that can recognize differences in salience between individual subordinate clauses". Their method involves the representation of the semantic structure of a document in the form of a binary tree, or hierarchical discourse tree.…”

Section: Structure-based Analysismentioning

confidence: 99%

Conventional and Structure Based Sentiment Analysis: A Survey

Ali

Gegov

Haig

et al. 2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Sentiment Analysis is a strand of Natural Language Processing that deals with the emotional polarity a given piece of text has. To gain this understanding from just a string of words, we must first consider a suitable way to break down the text to further classify what each part means. This can be done in a plethora of ways, which mostly stem from the understanding of a classifier. We believe that there is a large amount of information stored in structure-based features within the text, for instance, where the writer may place negation-terms not, neither and how this affects the overall polarity. Similarly, how words in sentences or sections, remote to the current-analysed section, may affect the polarity of said section. A combination of features from both a conventional and a structure-based understanding may also provide us with a larger accuracy in polarity. Therefore, this paper aims to explain both conventional sentiment analysis methods with structure-based methods as well as their practices, advantages and disadvantages concluding with how sentiment analysis can move forward with the appropriation of hybrid methods (methods involving motifs, practices and understandings) from conventional and structure-based methods, for classification.

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“…Sentiment analysis reveals personal opinions towards entities such as products, services, or events. This helps organizations and companies to improve their marketing, communication, production and acquisition [ 28 ].…”

Section: State Of the Artmentioning

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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Sentiment analysis based on rhetorical structure theory:Learning deep neural networks from discourse trees

Cited by 50 publications

References 39 publications

A joint text mining-rank size investigation of the rhetoric structures of the US Presidents’ speeches

A joint text mining-rank size investigation of the rhetoric structures of the US Presidents’ speeches

Conventional and Structure Based Sentiment Analysis: A Survey

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

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