Sentiment trend analysis in social web environments

Kwon, Kyunglag; Jeon, Yunwan; Cho, Chanho; Seo, Jongwoo; Chung, In-Jeong; Park, Han-Saem

doi:10.1109/bigcomp.2017.7881708

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Ahmad et al [73] presented an ACO based approach for expert identification and query routing in social networks. Kwon et al [74] proposed a novel method for sentiment trend analysis using ACO algorithm and SentiWordNet. They first collected social data in the form of Resource Description Framework (RDF) triples, and then used ACO algorithm to digitized the amassed RDF triples.…”

Section: Social Web Miningmentioning

confidence: 99%

When social computing meets soft computing: opportunities and insights

Hao

Park

Pei

2018

Hum. Cent. Comput. Inf. Sci.

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The characteristics of the massive social media data, diverse mobile sensing devices as well as the highly complex and dynamic user’s social behavioral patterns have led to the generation of huge amounts of high dimension, uncertain, imprecision and noisy data from social networks. Thanks to the emerging soft computing techniques which unlike the conventional hard computing. It is widely used for coping with the tolerant of imprecision, uncertainty, partial truth, and approximation. One of the most important and promising applications is social network analysis (SNA) that is the process of investigating social structures and relevant properties through the use of network and graph theories. This paper aims to survey various SNA approaches using soft computing techniques such as fuzzy logic, formal concept analysis, rough sets theory and soft set theory. In addition, the relevant software packages about SNA are clearly summarized.

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Section: Social Web Miningmentioning

confidence: 99%

When social computing meets soft computing: opportunities and insights

Hao

Park

Pei

2018

Hum. Cent. Comput. Inf. Sci.

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Claudio Cusano . models in financial or insurance fields [3] and of course, mining of social networks or sentiment analysis [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Non-Query-Based Pattern Mining and Sentiment Analysis for Massive Microblogging Online Texts

2020

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Pattern mining has been widely studied in the last decade given its great interest for research and its numerous applications in the real world. In this paper the definition of query and non-query based systems is proposed, highlighting the needs of non-query based systems in the era of Big Data. For this, we propose a new approach of a non-query based system that combines association rules, generalized rules and sentiment analysis in order to catalogue and discover opinion patterns in the social network Twitter. Association rules have been previously applied for sentiment analysis, but in most cases, they are used once the process of sentiment analysis is finished to see which tokens appear commonly related to a certain sentiment. On the other hand, they have also been used to discover patterns between sentiments. Our work differs from these in that it proposes a non-query based system which combines both techniques, in a mixed proposal of sentiment analysis and association rules to discover patterns and sentiment patterns in microblogging texts. The obtained rules generalize and summarize the sentiments obtained from a group of tweets about any character, brand or product mentioned in them. To study the performance of the proposed system, an initial set of 1.7 million tweets have been employed to analyse the most salient sentiments during the American pre-election campaign. The analysis of the obtained results supports the capability of the system of obtaining association rules and patterns with great descriptive value in this use case. Parallelisms can be established in these patterns that match perfectly with real life events.

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