Interpreting Reputation Through Frequent Named Entities in Twitter

Bennacer, Nacéra; Bugiotti, Francesca; Hewasinghage, Moditha; Isaj, Suela; Quercini, Gianluca

doi:10.1007/978-3-319-68783-4_4

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…This work extends the paper we presented in [3]: (1) including more related work; (2) giving more details about the algorithms, specifically about the sampling algorithm and its parameters; (3) studying the correlation between parameters of interest and their relation to the entities; (4) and finally, extending the experiments by analyzing another kind of entity of interest, products.…”

Section: Introductionmentioning

confidence: 60%

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A Frequent Named Entities-Based Approach for Interpreting Reputation in Twitter

et al. 2018

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Twitter is a social network that provides a powerful source of data. The analysis of those data offers many challenges among those stands out the opportunity to find reputation of a product, a person or any other entity of interest. Several approaches for sentiment analysis have been proposed in the literature to assess the general opinion expressed in tweets on an entity. Nevertheless, these methods aggregate sentiment scores retrieved from tweets, which is a static view to evaluate the overall reputation of an entity. The reputation of an entity is not static; entities collaborate with each other, and they get involved in different events over time. A simple aggregation of sentiment scores is then not sufficient to represent this dynamism. In this paper, we present a new approach to determine the reputation of an entity on the basis of the set of events in which it is involved. To achieve this, we propose a new sampling method driven by a tweet weighting measure to give a better quality and summary of the target entity. We introduce the concept of Frequent Named Entities to determine the events involving the target entity. Our evaluation achieved for different entities shows that 90% of the reputation of an entity originates from the events it is involved in and the breakdown into events allows interpreting the reputation in a transparent and self-explanatory way.

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Section: Introductionmentioning

confidence: 60%

“…To collect data about a certain topic, we used a query having a string as parameter (such as Obama). In [3]…”

Section: Methodsmentioning

confidence: 99%

A Frequent Named Entities-Based Approach for Interpreting Reputation in Twitter

et al. 2018

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“…The latter uses queries with a specific location. Another method of querying [not necessarily crawling] is keyword-based querying, widely used by the research on topic mining, opinion mining, the reputation of entities, quality of samples and several related topics [2,18,28] but not in research on geo-social topics.…”

Section: Related Workmentioning

confidence: 99%

Seed-Driven Geo-Social Data Extraction

Isaj

Pedersen

2019

Proceedings of the 16th International Symposium on Spatial and Temporal Databases

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Geo-social data has been an attractive source for a variety of problems such as mining mobility patterns, link prediction, location recommendation, and influence maximization. However, new geo-social data is increasingly unavailable and suffers several limitations. In this paper, we aim to remedy the problem of effective data extraction from geo-social data sources. We first identify and categorize the limitations of extracting geo-social data. In order to overcome the limitations, we propose a novel seed-driven approach that uses the points of one source as the seed to feed as queries for the others. We additionally handle differences between, and dynamics within the sources by proposing three variants for optimizing search radius. Furthermore, we provide an optimization based on recursive clustering to minimize the number of requests and an adaptive procedure to learn the specific data distribution of each source. Our comprehensive experiments with six popular sources show that our seed-driven approach yields 14.3 times more data overall, while our request-optimized algorithm retrieves up to 95% of the data with less than 16% of the requests. Thus, our proposed seed-driven approach set new standards for effective and efficient extraction of geo-social data.

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“…Typically, this type of crawling has not been used for location-based data, but rather on works on text mining, opinion mining, named entities, constructing the reputation of an entity, etc. [59][60][61]. However, querying with the location's name might return data that is geo-located in the area indicated by the keyword, but the results might also contain irrelevant data.…”

Section: Keyword-based Crawlingmentioning

confidence: 99%

Multi-Source Spatial Entity Extraction and Linkage

Isaj

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Interpreting Reputation Through Frequent Named Entities in Twitter

Cited by 3 publications

References 16 publications

A Frequent Named Entities-Based Approach for Interpreting Reputation in Twitter

A Frequent Named Entities-Based Approach for Interpreting Reputation in Twitter

Seed-Driven Geo-Social Data Extraction

Multi-Source Spatial Entity Extraction and Linkage

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