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

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%

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

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