Towards Application of Speech Act Theory to Opinion Mining

Pluwak, Agnieszka

doi:10.11649/cs.2016.004

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…First of all, most approaches to sentiment analysis (visible in the discussion of several works Liu quotes) focus on explicit expressions of evaluation, such as This product is really bad [21]. However, opinions expressed implicitly can comprise from 50% of analyzed mentions [24] up to over 88% [21]. Quite often, users provide their opinions through descriptions of their product experiences to encourage other users to evaluate the situation themselves.…”

Section: Sentiment Analysismentioning

confidence: 99%

Assessing the situation in the face of crisis

Pluwak,

Kacprzak,

Stańczyk

et al. 2023

Soc. Netw. Anal. Min.

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and tested three different approaches to crisis detection (a HANbased emotion detection model, heuristic crisis detection models for predefined risks, a statistical mention peak analysis tool with an ML-based summarization algorithm) to track most Internet sources and cover both explicit and implicit content and performed an analysis of 15 reputational crises in online sources in Poland. We offer a comparative analysis of NLP tools of qualitative semantic methods applied to a study of real-life reputational crises that appeared in Poland within the last two decades.

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Section: Sentiment Analysismentioning

confidence: 99%

Assessing the situation in the face of crisis

Pluwak,

Kacprzak,

Stańczyk

et al. 2023

Soc. Netw. Anal. Min.

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Assessing Situation in the Face of Crisis

Pluwak

Kacprzak

Lew

et al. 2022

Preprint

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From the perspective of marketing studies, a crisis of reputation is interpreted as such post factum, when measurable financial loss is induced. However, it is highly demanded to discover its signs as early as possible for risk management purposes. Here is where artificial intelligence finds its application since the start of the Facebook era. Emotion- or sentiment-classification algorithms, based on BiLSTM neural networks or transformer architectures, achieve very good F1 scores. Nevertheless, the scholarly literature offers very few approaches to the detection of reputational crises ante factum from an NLP point of view. At the same time, not every peak of mentions with negative sentiment equals a crisis of reputation by definition. There exist ample general sentiment classification tools dedicated to a specific social medium, e.g. Twitter, while reputational crises often expand over various Internet sources. However, they also tend to be highly unpredictable in the way they appear and spread online. Moreover, very few studies of their development have so far been conducted from the perspective of NLP tool-design. Therefore, in our work we try to answer the question: how can we track reputational crises fast and precisely in multiple communication channels, and what do current NLP methods offer with this respect? For this purpose we have: consulted Internet monitoring experts and, defined major crisis topics for three business domains, built and tested three different approaches to crisis detection (a HAN-based emotion detection model, heuristic crisis detection models for predefined risks, a statistical mention peak analysis tool with an ML-based summarization algorithm) to track most Internet sources and cover both explicit and implicit content and performed an analysis of 15 reputational crises in online sources in Poland. We offer a comparative analysis of NLP tools of qualitative semantic methods applied to a study of real-life reputational crises that appeared in Poland within the last two decades.

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From Smart City to Smart Society: A quality-of-life ontological model for problem detection from user-generated content

Periñán-Pascual

2023

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Social-media platforms have become a global phenomenon of communication, where users publish content in text, images, video, audio or a combination of them to convey opinions, report facts that are happening or show current situations of interest. Smart-city applications can benefit from social media and digital participatory platforms when citizens become active social sensors of the problems that occur in their communities. Indeed, systems that analyse and interpret user-generated content can extract actionable information from the digital world to improve citizens’ quality of life. This article aims to model the knowledge required for automatic problem detection to reproduce citizens’ awareness of problems from the analysis of text-based user-generated content items. Therefore, this research focuses on two primary goals. On the one hand, we present the underpinnings of the ontological model that categorises the types of problems affecting citizens’ quality of life in society. In this regard, this study contributes significantly to developing an ontology based on the social-sensing paradigm to support the advance of smart societies. On the other hand, we describe the architecture of the text-processing module that relies on such an ontology to perform problem detection, which involves the tasks of topic categorisation and keyword recognition.

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Towards Application of Speech Act Theory to Opinion Mining

Cited by 3 publications

References 16 publications

Assessing the situation in the face of crisis

Assessing the situation in the face of crisis

Assessing Situation in the Face of Crisis

From Smart City to Smart Society: A quality-of-life ontological model for problem detection from user-generated content

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