The Impact of Sentiment-driven Feedback on Knowledge Reuse in Online Communities

Grigore, Mihai; Rosenkranz, Christoph; Sutanto, Juliana

doi:10.17705/1thci.00073

Cited by 4 publications

(1 citation statement)

References 103 publications

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“…There has been a recent inclination towards integrating personality trait profiles into social bots. These profiles, once integrated and stored in memory-enhanced neural networks, enable the generation of more engaging responses than those produced by characterfree models [22], [23]. Dialogue agents can maintain a consistent style across multiple communication rounds between users and stylized chatbots.…”

Section: Related Workmentioning

confidence: 99%

Dialogue Agents with Literary Character Personality Traits

Zhao,

Liang,

Rusnachenko

2023

2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

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To enhance the engagement of chatbots, we need to imbue them with unique personalities and speech patterns. However, creating a high-quality conversational training dataset for this task can be time-consuming and labor-intensive. To address this, we propose an automated system that uses character dialogues from literary works. We used the Chinese classic, 《红楼梦》 (Dream of the Red Chamber), as our data source. Our system efficiently extracts dialogues and personality traits from the book, creates a personality map for each character, and generates responses that reflect these traits. It allows us to utilize the distinct linguistic styles that authors assign to characters with different personalities in classic books, making chatbots more human-like. Our evaluations demonstrate the efficacy of our system, with chatbots possessing personality traits proving more engaging than those lacking them. For transparency, we have made all our datasets and models available 1 .

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Section: Related Workmentioning

confidence: 99%

Dialogue Agents with Literary Character Personality Traits

Zhao,

Liang,

Rusnachenko

2023

2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

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A Comparison of Commercial Sentiment Analysis Services

et al. 2023

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Empirical insights into promising commercial sentiment analysis solutions that go beyond the claims of their vendors are rare. Moreover, due to the constant evolution in the field, previous studies are far from reflecting the current situation. The goal of this article is to evaluate and compare current solutions using two experimental studies. In the first part of the study, based on tweets about airline service quality, we test the solutions of six vendors with different market power, such as Amazon, Google, IBM, Microsoft, Lexalytics, and MeaningCloud, and report their measures of accuracy, precision, recall, (macro)F1, time performance, and service level agreements (SLA). Furthermore, we compare two of the services in depth with multiple data sets and over time. The services tested here are Google Cloud Natural Language API and MeaningCloud Sentiment Analysis API. For evaluating the results over time, we use the same data set as in November 2020. In addition, further topic-specific and general Twitter data sets are used. The experiments show that the IBM Watson NLU and Google Cloud Natural Language API solutions may be preferred when negative text detection is the primary concern. When tested in July 2022, the Google Cloud Natural Language API was still the clear winner compared to the MeaningCloud Sentiment Analysis API, but only on the airline service quality data set; on the other data sets, both services provided specific benefits and drawbacks. Furthermore, we detected changes in the sentiment classification over time with both services. Our results motivate that an independent, critical, and longitudinal experimental analysis of sentiment analysis services can provide interesting insights into their overall reliability and particular classification accuracy beyond marketing claims to critically compare solutions based on real data and analyze potential weaknesses and margins of error before making an investment.

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The Use of Crowdsourcing and Social Media in Accounting Research

Firoozi

2017

Journal of Information Systems

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In this study, we investigate the use of crowdsourcing websites in accounting research. Our analysis shows that the use of crowdsourcing in accounting research is relatively low, and these websites have been mainly used to collect data through surveys and for conducting experiments. Next, we compare and discuss papers related to crowdsourcing in the accounting area with research in computer science (CS) and information systems (IS), which are more advanced in using crowdsourcing websites. We then focus on Amazon Mechanical Turk as one of the most widely used crowdsourcing websites in academic research to investigate what type of tasks can be done through this platform. Based on our task analysis, one of the areas in accounting research that can benefit from crowdsourcing websites is research on social media content. Therefore, we then discuss how research in CS, IS, and crowdsourcing websites can help researchers improve their work on social media.

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The Impact of Sentiment-driven Feedback on Knowledge Reuse in Online Communities

Cited by 4 publications

References 103 publications

Dialogue Agents with Literary Character Personality Traits

Dialogue Agents with Literary Character Personality Traits

A Comparison of Commercial Sentiment Analysis Services

The Use of Crowdsourcing and Social Media in Accounting Research

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