Machine Learning Methods for Opinion Mining In text: The Past and the Future

Kolovou, Athanasia

doi:10.1007/978-3-030-15628-2_13

Cited by 2 publications

(3 citation statements)

References 48 publications

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“…Words that implicitly convey behavior data are incorporated into the views to ascertain the sentiment words' true behavior [32].…”

Section: Incorporated Implicit Behavior Data In Opinionmentioning

confidence: 99%

A Review on Challenges in Recent Opinion Extraction Techniques

Kumar,

Ahamad

2024

International Journal of Innovative Research in Computer Science and Technology (IJIRCST)

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With the introduction of Web 2.0, people are now encouraged to share information about events in their community as well as to voice their opinions and beliefs. People utilize social media, blogs, review sites, news sites, user feedback portals, and other platforms to express themselves regarding events, locations, decisions made by leaders, policies, and other topics. Opinion extraction is a method that automatically extracts and analyzes people's sentiments from their thoughts. Manufacturers may find this collected data helpful in analyzing their products. Sentiment analysis, also referred to as opinion extraction, has been the subject of intense research over the past 15 years by academics, research communities, and service corporations in an effort to gather and examine public opinions and moods. This work focuses on the difficulties encountered in opinion extraction at many levels, including word-, sentence-, and language-related difficulties as well as some generic difficulties. The study provided clear examples and a thorough discussion of 30 hurdles, which aid in presenting problems for text or opinion extraction research. Opinion extraction tools are briefly presented in this section to help novice researchers get started. The published material spans the years 2004 to 2022.

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“…Words that implicitly convey behavior data are incorporated into the views to ascertain the sentiment words' true behavior [32].…”

Section: Incorporated Implicit Behavior Data In Opinionmentioning

confidence: 99%

A Review on Challenges in Recent Opinion Extraction Techniques

Kumar,

Ahamad

2024

International Journal of Innovative Research in Computer Science and Technology (IJIRCST)

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“…International Negotiation 27 (2022) 1-32 sentiments expressed by conflict parties and stakeholders in large amounts of text (Kolovou 2019). For example, the United Nations, in collaboration with academic and private sector partners, have experimented with systems that analyze a large number of responses from mass online focus groups to map public opinion (ITU 2019; Warrell 2020), or that help analyze large amounts of notes from dialogue efforts according to topics and conflict party positions (Arana-Catania, Van Lier & Procter 2021).…”

Section: The Machine Hubris: Technocentric Peacemakingmentioning

confidence: 99%

“…First, by working on the conflict parties' truth claims about the world, we can assess how statements, narratives or discourses create a specific meaningful reality for them. This question can be tackled with support of the broad spectrum of ML-driven text mining and analysis tools, including the variants of sentiment, opinion and argument analysis (Kolovou 2019). Efforts to mine the conflict parties' or stakeholders' position from data collected in the course of dialogue efforts or through online focus groups constitute a valuable first step in this direction.…”

Section: Beyond Neutrality and Bias: Hybrid Intelligence And The Hermeneutics Of Peacemakingmentioning

confidence: 99%

When Mediators Need Machines (and Vice Versa): Towards a Research Agenda on Hybrid Peacemaking Intelligence

Hirblinger

2022

Int. Negot.

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The growing use of artificial intelligence (AI) in peace processes challenges the ways in which mediators conventionally generate and use knowledge to facilitate a convergence of conflict party positions. Mediators are increasingly unable to maintain their role as information brokers without the support of automated information gathering and analysis systems. However, the effectiveness of such AI-tools is likewise hampered by their limited autonomy across peacemaking contexts, by the paradigmatic nature of knowledge that is used to design AI-systems, and the influence of subjective factors that are difficult to measure. These challenges may be overcome by hybrid human-machine systems in support of peacemaking. This article formulates a research agenda for hybrid peacemaking intelligence that is concerned with the increasing interdependence of humans and machines. It suggests studying how agency is distributed in mediator-machine networks, the hermeneutics of machine-supported participatory approaches, and how hybrid arrangements change power relations in peacemaking.

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Machine Learning Methods for Opinion Mining In text: The Past and the Future

Cited by 2 publications

References 48 publications

A Review on Challenges in Recent Opinion Extraction Techniques

A Review on Challenges in Recent Opinion Extraction Techniques

When Mediators Need Machines (and Vice Versa): Towards a Research Agenda on Hybrid Peacemaking Intelligence

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