A comparison between semi-supervised and supervised text mining techniques on detecting irony in greek political tweets

Charalampakis, Basilis; Spathis, Dimitris; Kouslis, Elias; Kermanidis, Katia Lida

doi:10.1016/j.engappai.2016.01.007

Cited by 50 publications

(21 citation statements)

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“…This percentage is similar to the 29% that was detected in a collection of tweets regarding the candidates of the Republican party who were running for the US Presidential nomination for the same elections [ 50 ]. Nevertheless, the only other study (to our knowledge) that attempted to identify sarcasm in greek political tweets, was performed in a much smaller (44,000 tweets) dataset referring to the Greek legislative elections of 2012 and concluded that 54.5% of tweets are sarcastic [ 51 ]. Since the subject of a tweet is a very strong indicator of sarcasm it is difficult to obtain a ground-truth regarding sarcasm percentages.…”

Section: Resultsmentioning

confidence: 99%

Social media analysis during political turbulence

et al. 2017

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Today, a considerable proportion of the public political discourse on nationwide elections proceeds in Online Social Networks. Through analyzing this content, we can discover the major themes that prevailed during the discussion, investigate the temporal variation of positive and negative sentiment and examine the semantic proximity of these themes. According to existing studies, the results of similar tasks are heavily dependent on the quality and completeness of dictionaries for linguistic preprocessing, entity discovery and sentiment analysis. Additionally, noise reduction is achieved with methods for sarcasm detection and correction. Here we report on the application of these methods on the complete corpus of tweets regarding two local electoral events of worldwide impact: the Greek referendum of 2015 and the subsequent legislative elections. To this end, we compiled novel dictionaries for sentiment and entity detection for the Greek language tailored to these events. We subsequently performed volume analysis, sentiment analysis, sarcasm correction and topic modeling. Results showed that there was a strong anti-austerity sentiment accompanied with a critical view on European and Greek political actions.

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Section: Resultsmentioning

confidence: 99%

Social media analysis during political turbulence

et al. 2017

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“…It is very complex to create a completely automatic system, so that a reasonable choice in this kind of scenarios is to create a semi-supervised approach. On the one hand, this strategy can be checked in the literature to be a suitable approach to big data problems in emerging areas, in which data quality is revealed in those early stages to have an even more-than-suspected potential high impact on the company [ 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 ]. On the other hand, the Information Technology areas or similar ones often have staff partially devoted and responsible for the data quality aspects.…”

Section: Discussionmentioning

confidence: 99%

Entropic Statistical Description of Big Data Quality in Hotel Customer Relationship Management

Serrano

Ballestero

Muñoz-Romero

et al. 2019

Entropy

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Customer Relationship Management (CRM) is a fundamental tool in the hospitality industry nowadays, which can be seen as a big-data scenario due to the large amount of recordings which are annually handled by managers. Data quality is crucial for the success of these systems, and one of the main issues to be solved by businesses in general and by hospitality businesses in particular in this setting is the identification of duplicated customers, which has not received much attention in recent literature, probably and partly because it is not an easy-to-state problem in statistical terms. In the present work, we address the problem statement of duplicated customer identification as a large-scale data analysis, and we propose and benchmark a general-purpose solution for it. Our system consists of four basic elements: (a) A generic feature representation for the customer fields in a simple table-shape database; (b) An efficient distance for comparison among feature values, in terms of the Wagner-Fischer algorithm to calculate the Levenshtein distance; (c) A big-data implementation using basic map-reduce techniques to readily support the comparison of strategies; (d) An X-from-M criterion to identify those possible neighbors to a duplicated-customer candidate. We analyze the mass density function of the distances in the CRM text-based fields and characterized their behavior and consistency in terms of the entropy and of the mutual information for these fields. Our experiments in a large CRM from a multinational hospitality chain show that the distance distributions are statistically consistent for each feature, and that neighbourhood thresholds are automatically adjusted by the system at a first step and they can be subsequently more-finely tuned according to the manager experience. The entropy distributions for the different variables, as well as the mutual information between pairs, are characterized by multimodal profiles, where a wide gap between close and far fields is often present. This motivates the proposal of the so-called X-from-M strategy, which is shown to be computationally affordable, and can provide the expert with a reduced number of duplicated candidates to supervise, with low X values being enough to warrant the sensitivity required at the automatic detection stage. The proposed system again encourages and supports the benefits of big-data technologies in CRM scenarios for hotel chains, and rather than the use of ad-hoc heuristic rules, it promotes the research and development of theoretically principled approaches.

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“…The binomial proportion is defined as the number of successes divided by the number of trials. This method was used to classify schema for irony detection in Greek political tweets on Twitter [16]. Our hypothesis states that the positive rate of a post is observed as the binomial proportion [17]; we can compute that rate.…”

Section: Related Workmentioning

confidence: 99%

Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †

et al. 2020

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Influencer marketing is a modern method that uses influential users to approach goal customers easily and quickly. An online social network is a useful platform to detect the most effective influencer for a brand. Thus, we have an issue: how can we extract user data to determine an influencer? In this paper, a model for representing a social network based on users, tags, and the relationships among them, called the SNet model, is presented. A graph-based approach for computing the impact of users and the speed of information propagation, and measuring the favorite brand of a user and sharing the similar brand characteristics, called a passion point, is proposed. Therefore, we consider two main influential measures, including the extent of the influence on other people by the relationships between users and the concern to user’s tags, and the tag propagation through social pulse on the social network. Based on these, the problem of determining the influencer of a specific brand on a social network is solved. The results of this method are used to run the influencer marketing strategy in practice and have obtained positive results.

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A comparison between semi-supervised and supervised text mining techniques on detecting irony in greek political tweets

Cited by 50 publications

References 14 publications

Social media analysis during political turbulence

Social media analysis during political turbulence

Entropic Statistical Description of Big Data Quality in Hotel Customer Relationship Management

Detecting the Influencer on Social Networks Using Passion Point and Measures of Information Propagation †

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