Overview of RepLab 2014: Author Profiling and Reputation Dimensions for Online Reputation Management

Amigó, Enrique; Carrillo-de-Albornoz, Jorge; Chugur, Irina; Corujo, Adolfo; Gonzalo, Julio; Meij, Edgar; Rijke, Maarten de; Spina, Damiano

doi:10.1007/978-3-319-11382-1_24

Cited by 51 publications

(66 citation statements)

References 1 publication

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“…There are also some public tools with this purpose, such as SocialMention 6 . In addition, there have been several studies in calculate the reputation of an entity, this is, how an entity is being valued in the Internet (Villena-Román et al, 2012;Amigó et al, 2014). Our proposal contains different statistical visualisations, reputation calculation, and advanced filtering by different dimensions, everything in real-time.…”

Section: Related Workmentioning

confidence: 99%

Opinion Mining in Social Networks versus Electoral Polls

Fernández

Llopis

Gutiérrez

et al. 2017

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning

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The recent failures of traditional poll models, like the predictions in United Kingdom with the Brexit, or in United States presidential election with the victory of Donald Trump, have been noteworthy. With the decline of traditional poll models and the growth of the social networks, automatic tools are gaining popularity to make predictions in this context. In this paper we present our approximation and compare it with a real case: the 2017 French presidential election.

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Section: Related Workmentioning

confidence: 99%

Opinion Mining in Social Networks versus Electoral Polls

Fernández

Llopis

Gutiérrez

et al. 2017

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning

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“…3 These tasks were organized as a CLEF evaluation task [8,9] where teams were given a set of entities and for each entity a set of tweets were provided. In RepLab 2013 the challenge was to classify tweets as relevant or irrelevant with respect to the entity, and in RepLab 2014, the challenge was to classify tweets with respect to the reputation dimension of an entity.…”

Section: Thesis Flow and Structurementioning

confidence: 99%

“…Finally, we present the aggregation of these measures into a single measure. Note that we use non-normalized versions of relatedness measures as the range of values for Wikipedia category-article based heuristics is not wide, and moreover, we wish to capture even subtle relationships between the concepts represented by the textual units 9 . In the formulations presented below we use the notation of 1) p to denote the candidate phrase for which a relatedness measure is to be calculated, and 2) cat t to denote the category taxonomy corresponding to the entity under consideration.…”

Section: Relatedness Measuresmentioning

confidence: 99%

“…The campaigns were organized as CLEF evaluation tasks for three consecutive years i.e. RepLab 2012 [10], RepLab 2013 [8] and RepLab 2014 [9] where teams were given a set of entities and for each entity a set of tweets were provided. The challenge was to perform mining over these tweets for various tasks relevant to online reputation management of the given entities.…”

Section: Significant Subtasks Within Online Reputation Managementmentioning

confidence: 99%

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Utilising Wikipedia for Text Mining Applications

Qureshi

2016

SIGIR Forum

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The process whereby inferences are made from textual data is broadly referred to as text mining. In order to ensure the quality and effectiveness of the derived inferences, several approaches have been proposed for different text mining applications. Among these applications, classifying a piece of text into pre-defined classes through the utilisation of training data falls into supervised approaches while arranging related documents or terms into clusters falls into unsupervised approaches. In both these approaches, processing is undertaken at the level of documents to make sense of text within those documents. Recent research efforts have begun exploring the role of knowledge bases in solving the various problems that arise in the domain of text mining. Of all the knowledge bases, Wikipedia on account of being one of the largest human-curated, online encyclopaedia has proven to be one of the most valuable resources in dealing with various problems in the domain of text mining. However, previous Wikipedia-based research efforts have not taken both Wikipedia categories and Wikipedia articles together as a source of information. This thesis serves as a first step in eliminating this gap and throughout the contributions made in this thesis, we have shown the effectiveness of Wikipedia category-article structure for various text mining tasks. Wikipedia categories are organized in a taxonomical manner serving as semantic tags for Wikipedia articles and this provides a strong abstraction and expressive mode of knowledge representation. In this thesis, we explore the effectiveness of this mode of Wikipedia's expression (i.e., the category-article structure) via its application in the domains of text classification, subjectivity analysis (via a notion of "perspective" in news search), and keyword extraction. First, we show the effectiveness of exploiting Wikipedia for two classification tasks i.e., 1- classifying the tweets1 being relevant/irrelevant to an entity or brand, 2- classifying the tweets into different topical dimensions such as tweets related with workplace, innovation, etc. To do so, we define the notion of relatedness between the text in tweet and the information embedded within the Wikipedia category-article structure. Then, we present an application in the area of news search by using the same notion of relatedness to show more information related to each search result highlighting the amount perspective or subjective bias in each returned result towards a certain opinion, topical drift, etc. Finally, we present a keyword extraction strategy using community detection over the Wikipedia categories to discover related keywords arranged in different communities. The relationship between Wikipedia categories and articles is explored via a textual phrase matching framework whereby the starting point is textual phrases that match Wikipedia articles' titles/redirects. The Wikipedia articles for which a match occurs are then utilised by extraction of their associated categories, and these Wikipedia categories are used to derive various structural measures such as those relating to taxonomical depth and Wikipedia articles they contain. These measures are utilised in our proposed text classification, subjectivity analysis, and keyword extraction framework and the performance is analysed via extensive experimental evaluations. These experimental evaluations undertake comparisons with standard text mining approaches in the literature and our Wikipedia framework based on its category-article structure outperforms the standard text mining techniques.

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“…Modern systems and SA's state of the art can be seen in NLP Workshops and competitive challenges, such as RepLab [7], SemEval Twitter SA [8], and aspect based SA tasks [9,10]. These events attract participants around the world, but most focused in the English language.…”

Section: Related Workmentioning

confidence: 99%

Combining Overall and Target Oriented Sentiment Analysis over Portuguese Text from Social Media

Saias

Silva²,

Oliveira³

et al. 2015

TMLAI

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This document describes an approach to perform sentiment analysis on social media Portuguese content. In a single system, we perform polarity classification for both the overall sentiment, and target oriented sentiment. In both modes we train a Maximum Entropy classifier. The overall model is based on BoW type features, and also features derived from POS tagging and from sentiment lexicons. Target oriented analysis begins with named entity recognition, followed by the classification of sentiment polarity on these entities. This classifier model uses features dedicated to the entity mention textual zone, including negation detection, and the syntactic function of the target occurrence segment. Our experiments have achieved an accuracy of 75% for target oriented polarity classification, and 97% in overall polarity.

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Overview of RepLab 2014: Author Profiling and Reputation Dimensions for Online Reputation Management

Cited by 51 publications

References 1 publication

Opinion Mining in Social Networks versus Electoral Polls

Opinion Mining in Social Networks versus Electoral Polls

Utilising Wikipedia for Text Mining Applications

Combining Overall and Target Oriented Sentiment Analysis over Portuguese Text from Social Media

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