International audienceWith the explosion of Web 2.0, people are becoming more communicative through expansion of services and multi-platform applications such as microblogs, forums and social networks which establishes social and collabora-tive backgrounds. These services can be seen as very large information repository containing millions of text messages usually organized into complex networks involving users interacting with each other at specific times. Several works focused only to retrieve separate tweets or those sharing same hashtags, but, it is not powerful enough if the goal of the search is to retrieve relevant tweets based on content. In addition, finding good results concerning the given subjects needs to consider the entire context. However, context can be derived from user interactions. In this work, we propose a new method to retrieval conversation on microblogging sites. It's based on content analysis and content enrichment. The goal of our method is to present a more informative result compared to conventional search engine. To valid our method, we developed the TCOND system (Twitter Conversation Detector) which offers an alternative, results to keyword search on twitter and google. We have evaluated our method on collected social network corpus related to specific subjects, and we obtained good results

In this paper, we propose TunDiaWN (Tunisian dialect Wordnet) a lexical resource for the dialect language spoken in Tunisia. Our TunDiaWN construction approach is founded, in one hand, on a corpus based method to analyze and extract Tunisian dialect words. A clustering technique is adapted and applied to mine the possible relations existing between the Tunisian dialect extracted words and to group them into meaningful groups. All these suggestions are then evaluated and validated by the experts to perform the resource enrichment task. We reuse other Wordnet versions, mainly for English and Arabic language to propose a new database structure enriched by innovative features and entities.

International audienceLast years, people are becoming more communicative through expansion of services and multi-platform applications such as blogs, forums and social networks which establishes social and collabo-rative backgrounds. These services like Twitter, which is the main domain used in our work can be seen as very large information repository containing millions of text messages usually organized into complex networks involving users interacting with each other at specific times. Several works have proposed tools for tweets search focused only to retrieve the most recent but relevant tweets that address the information need. Therefore, users are unable to explore the results or retrieve more relevant tweets based on the content and may get lost or become frustrated by the information overload. In addition, finding good results concerning the given subjects needs to consider the entire context. However, context can be derived from user interactions. In this work, we propose a new method to retrieval conversation on mi-croblogging sites. It's based on content analysis and content enrichment. The goal of our method is to present a more informative result compared to conventional search engine. The proposed method has been implemented and evaluated by comparing it to Google and Twitter Search engines and we obtained very promising results

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