Users' Traces for Enhancing Arabic Facebook Search

International audienceThis paper proposes an approach on Facebook search in Arabic, which exploits several users' traces (e.g. comment, share, reactions) left on Facebook posts to estimate their social importance. Our goal is to show how these social traces (signals) can play a vital role in improving Arabic Facebook search. Firstly, we identify polarities (positive or negative) carried by the textual signals (e.g. comments) and non-textual ones (e.g. the reactions love and sad) for a given Facebook post. Therefore, the polarity of each comment expressed on a given Facebook post, is estimated on the basis of a neural sentiment model in Arabic language. Secondly, we group signals according to their complementarity using features selection algorithms. Thirdly, we apply learning to rank (LTR) algorithms to re-rank Facebook search results based on the selected groups of signals. Finally, experiments are carried out on 13,500 Facebook posts, collected from 45 topics in Arabic language. Experiments results reveal that Random Forests combined with ReliefFAttributeEval (RLF) was the most effective LTR approach for this task

SSwWS: structural model of information architecture

The Web Technologies allow a representation of a domain of knowledge. This facilitates the conversion of an explicit and tacit knowledge to the possibility of adding knowledge to the Web for automatic processing by the computer. For this reason, it has been designed to be an architecture known as SSwWS (Search Semantic with Web Services) or Search Semantic Web Services, to show how to extend the functionality of the Web search and semantic raised by Berners-Lee, on the meta-references, defined in a Web ontology, so that a user on the Internet can find the answers to their questions through Web services in a simple and fast

Sentiment classification of Arabic documents ::experiments with multi-type features and ensemble algorithms

Document sentiment classification is often processed by applying machine learning techniques, in particular supervised learning which consists basically of two major steps: feature extraction and training the learning model. In the literature, most existing researches rely on n-grams as selected features, and on a simple basic classifier as learning model. In the context of our work, we try to improve document classification findings in Arabic sentiment analysis by combining different types of features such as opinion and discourse features; and by proposing an ensemble-based classifier to investigate its contribution in Arabic sentiment classification. Obtained results attained 85.06% in terms of macro-averaged Fmeasure, and showed that discourse features have moderately improved Fmeasure by approximately 3% or 4%

EV energy management strategy based on a single converter fed by a hybrid battery/supercapacitor power source

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Sentiment classification at discourse segment level ::experiments on multi-domain Arabic corpus

Sentiment classification aims to determine whether the semantic orientation of a text is positive, negative or neutral. It can be tackled at several levels of granularity: expression or phrase level, sentence level, and document level. In the scope of this research, we are interested in the sentence and sub-sentential level classification which can provide very useful trends for information retrieval and extraction applications, Question Answering systems and summarization tasks. In the context of our work, we address the problem of Arabic sentiment classification at sub-sentential level by (i) building a high coverage sentiment lexicon with semi-automatic approach; (ii) creating a large multi-domain annotated sentiment corpus segmented into discourse segments in order to evaluate our sentiment approach; and (iii) applying a lexicon-based approach with an aggregation model taking into account advanced linguistic phenomena such as negation and intensification. The results that we obtained are considered good and close to state of the art results in English language

Towards a user-friendly solution for collaboratively managing a developed ontology

Ontologies are getting popular for knowledge representation because it is capable of representing the semantics of the knowledge. However, with the evolution of the knowledge, maintaining and support evolution of a developed ontology becomes a complex task. We can get help of domain experts to maintain the ontology as a solution. But, that approach has another problem which is often domain experts do not know about ontology concepts, languages and tools. Also, if we try to accomplish ontology maintenance by the help of domain experts, there should be a technique to maintain ontology collaboratively. In a collaborative ontology development environment, when one user modifying the ontology, other users should also aware of that modification. In order to achieve this awareness, keeping a history of modifications is required. Furthermore, one user’s modifications may conflict with others modifications; therefore, the ontology development system should support that kind of situations too. This study mainly concerns how to maintain the structure of a developed ontology collaboratively. This study follows synchronous collaborative technique by keeping ontology in a central server. Collaborative partners are able to modify and maintain the ontology through user-friendly web-based interfaces. Since the ontology keeps in central place every user knows what modifications happen to the ontology in real time. Also modifications are recorded in a relational database and users are allowed to access those change history when it needed. Versions of the ontology are generated based on modification types. If the modification affects backward compatibility then a new version is created and if not current version is updated. To distinguish different versions, semantic versioning standard is used. The implemented system is validated individually and evaluated by the help of a user group. Validation and evaluation results prove that system is performing as expected

Exploring Differences in the Impact of Users' Traces on Arabic and English Facebook Search

International audienceThis paper proposes an approach on Facebook search in Arabic and English, which exploits several users' traces (e.g. comment, share, reactions) left on Facebook posts to estimate their social importance. Our goal is to show how these social traces (signals) can play a vital role in improving Arabic and English Facebook search. Firstly, we identify polarities (positive or negative) carried by the textual signals (e.g. comments) and non-textual ones (e.g. the reactions love and sad) for a given Facebook posts. Therefore, the polarity of each comment expressed in Arabic or in English on a given Facebook post, is estimated on the basis of a neural sentiment model. Secondly , we group signals according to their complementarity using attributes (features) selection algorithms. Thirdly, we apply learning to rank (LTR) algorithms to re-rank Facebook search results based on the selected groups of signals. Finally, experiments are carried out on 13,500 Facebook posts, collected from 45 topics, for each of the two languages. Experiments results reveal that Random Forests was the most effective LTR approach for this task, and for the both languages. However, the best appropriate features selection algorithms are ReliefFAttributeEval and InfoGainAttributeEval for Arabic and English Facebook search task, respectively