Advertising recommendation system based on dynamic data analysis on Turkish speaking Twitter users

Online okruženja, a posebno društvene mreže postala su snažna alternative objavljivanju oglasa. Za učinkovito oglašavanje važno je da se sadržaj poistovjećuje s očekivanjima ciljane publike. Uzimajući u obzir da se očekivanja mogu s vremenom promijeniti, potrebno je u realnom vremenu i dinamički prepoznati orijentaciju korisnika. U ovom su se radu u realnom vremenu analizirale poruke turskih korisnika Twittera i identificirala njihova trenutna očekivanja. U tu je svrhu dizajnirana web usluga koja analizira profil korisnika i daje oglase koji najbolje odgovaraju očekivanjima. Za filtriranje odgovarajućeg sadržaja oglašavanja korištena je metoda nazvana heuristička metoda odstranjivanja suvišnog (Heuristic Pruning Method - HPM). Razvijeni sustav je testiran na grupi volontera, aktivnih korisnika Twittera, a učinkovitost sustava se pokazala dobivenom povratnom informacijom-feedbackom.Online environments and especially social networks have become a great alternative to advertisement publishing. In order to accomplish effective advertising it is important that the contents coincide with the expectations of the target audience. Considering that expectations may change over time, it is required to identify the orientation of the users in real time and dynamically. In this study, the messages shared by Turkish Twitter users were analysed in real time and the instant expectations of the users have been identified. To perform this work, a web service was designed which analyses the user’s profile and presents the advertisements that suit best to expectations. A method called Heuristic Pruning Method (HPM) has been revealed in order to filter the most appropriate advertising content. The developed system has been tested on a voluntary participant group who actively uses Twitter, and the effectiveness of the system is demonstrated by the received feedback

LA VISIBILITÀ ON-LINE DELLE IMPRESE ALBERGHIERE: UN POSSIBILE MODELLO DI MISURAZIONE

none4noIn a tourist scenario profoundly transformed by the advent of new online players, hotel companies are called today to exploit the online communication tools in a more and more conscious way. In this context, for hotel SMEs, it becomes fundamental to achieve a correct perception of the online presence in order to intervene in real time with specific web marketing actions. The research objectives are 1) to explore the firms’ online presence through an economic literature review 2) to define an online visibility measurement indicator. The research has provided for three sequential phases: 1) the economic literature analysis (to explore the online presence issue/ its principal dimensions) 2) creation of an Index of Online Visibility of Hotel 3) test through three successive measurements of the online visibility of all 227 Hotels of Cattolica. The research has produced three mutually coherent online visibility charts, showing, albeit with some limitations, the validity and the efficacy of the index.openMarco, Cioppi; Ilaria, Curina; Fabio, Forlani; Tonino, PencarelliCioppi, Marco; Curina, Ilaria; Forlani, Fabio; Pencarelli, Tonin

Discovering filter keywords for company name disambiguation in twitter

A major problem in monitoring the online reputation of companies, brands, and other entities is that entity names are often ambiguous (apple may refer to the company, the fruit, the sin ger, etc.). The prob- lem is particularly hard in microblogging services such as Twitter, where texts are very short and there is little context to disambiguate.In this paper we address the filtering task of determining, out of a set of tweets that contain a company name, which ones do refer to the company.Our approach relies on the identification of filter keywords : those whose presence in a tweet reliably confirm(positive keywords) or discard (negative keywords) that the tweet refers to the company. We describe an algorithm to extract filter keywords that does not use any previously annotated data about the target company. The algorithm allows to classify 58% of the tweets with 75% accuracy; and those can be used to feed a machine learning algorithm to obtain a complete classification of all tweets with an overall accuracy of 73%. In comparison, a 10-fold validation of the same machine learning algo- rithm provides an accuracy of 85%, i.e., our unsupervised algorithm has a 14% loss with respect to its supervised counterpart. Our study also shows that (i) filter keywords for Twitter does not directly derive from the public in for- mation about the company in the Web: a manual selection of keywords from relevant web sources only covers 15% of the tweets with 86% accuracy;(ii) filter keywords can indeed be a productive way of clas- sifying tweets: the five best possible keywords cover, in average,28% of the tweets for acompany inour test collection