VIPE: A NEW INTERACTIVE CLASSIFICATION FRAMEWORK FOR LARGE SETS OF SHORT TEXTS - APPLICATION TO OPINION MINING

International audienceThis paper presents a new interactive opinion mining tool that helps users to classify large sets of short texts originated from Web opinion polls, technical forums or Twitter. From a manual multi-label pre-classification of a very limited text subset, a learning algorithm predicts the labels of the remaining texts of the corpus and the texts most likely associated to a selected label. Using a fast matrix factorization, the algorithm is able to handle large corpora and is well-adapted to interactivity by integrating the corrections proposed by the users on the fly. Experimental results on classical datasets of various sizes and feedbacks of users from marketing services of the telecommunication company Orange confirm the quality of the obtained results

SmartNews: An Automatic Approach for Event Detection on Media Platforms

Social Media Platforms (SMPs) are currently the leading media data sources in the world; billions of people’s electronic devices have adopted these SMPs for their use. The users ‘ accounts on these platforms generate massive amounts of data daily. Data have become an essential building block for many organizations of different domains. Recently, media organizations started using social media as a principal source to collect data, mainly news. Having recognized the importance of SMPs and data availability, media organizations are not using these data efficiently. Many media organizations still use and analyze internet data, especially from social media, manually, which leads to many disadvantages. This research proposes a more efficient and automated approach to collecting information from social media. Actually, this paper proposes an integrated framework that can extract data from multiple SMPs and merge them, store them, and finally allow media workers to extract fundamental data (events) automatically and smartly from social media. The proposed framework takes input from a query and finds the following information: top tweets, total likes and retweets on this query, user’s identity, sentiment analysis, and finally, the prediction component that can classify if a particular item has classified an event or not. An advantage of this approach is to help media leaders control and track their performance in the media sector and maintain popularity on the internet. The proposed system has been validated on real datasets collected from different data sources. Findings show that this proposed system has remarkable accuracy, precision, and recall results, after evaluating different machine learning algorithms

Comparison of Social Media as a Platform for Financial Literacy Source

Financial literacy has been an essential aspect of financial inclusion. One of the recently explored drivers of financial literacy is social media. Social media has occupied most of the user's attention and thus become one of the primary sources of knowledge in modern society. This study purpose was to examine the context of social media in applying financial literacy planning and program. This study is descriptive by using a structured literature review approach to address social media in the context of financial literacy and distinguish h category of social media and its attribute regarding the application of financial literacy. This study found that social media has been categorized to put perspective on its usefulness as a financial literacy program platform. Every social media category has different uses; some are better suited for a specific financial literacy program. This study’s limitation was that it does not measure the effectiveness of the financial literacy program based on its social media platform. However, this study contributes to researchers and practitioners to better understand the dynamic of the financial literacy program through social media platforms

Insights of School Head About Marketing Education Services Through Digital Media

This study mainly focuses on the insights of a private school’s head pertaining to the use of digital media in educational marketing. The qualitative research paradigm was chosen for this study and in depth phenomenological interview was conducted from a head of a private school. Two themes were extracted from the data: Marketing educational services through digital media and its challenges, and digital media tool for marketing education services. The study revealed that the school head perceived the digital media to be cost-effective marketing strategy that was multidimensional and value-driven, but due to lack of awareness, skills, attitude, and sense of maturity among stakeholders, digital media was ignored and was not much used as a cost-effective marketing tool. Nevertheless, it is proposed that by hiring marketing personnel to promote services professionally, by aligning their strategies according to the demands of their customers, and by inviting customers’ voice on digital platforms, digital media can become a cost-effective and a valuable tool for mercerization of education

Social network analysis to support decision-making

The growing importance of decision-making within web context requires defining and implementing efficient mechanisms to support its activities. The social networks, seen as opportunities for organizational collaboration, allow people to interact across continents and time zones, expressing opinions and sharing information. They also constitute an increasingly used means for decision support, when seeking information, which can produce a massive amount. Therefore, it is necessary to analyze such information in order to establish opinions and points of view on a variety of issues that can play an important role for decision-making. As the web discourses (of opinion and information) often encompass knowledge construction, interaction and the consequent development of online social networks capturing and understanding all interactions can be extremely valuable for building indicators to support decision-making. Accordingly, the main objective of this work is to explore the interactions and discursive exchanges between social actors, in order to extract information towards decision support. We intend to investigate whether it is possible to structure collected data from online social networks, integrating human interaction and network structure, namely by using Social Network Analysis (SNA) to study the network from a duofold manner: the web discourse, which depends on the transmition of information; and the interaction among social actors, as information disseminators. Our work intends to determine whether social actors and their interactions are consistent with the creation of indicators fit for decision support.info:eu-repo/semantics/publishedVersio

Violence Detection in Social Media-Review

Social media has become a vital part of humans’ day to day life. Different users engage with social media differently. With the increased usage of social media, many researchers have investigated different aspects of social media. Many examples in the recent past show, content in the social media can generate violence in the user community. Violence in social media can be categorised into aggregation in comments, cyber-bullying and incidents like protests, murders. Identifying violent content in social media is a challenging task: social media posts contain both the visual and text as well as these posts may contain hidden meaning according to the users’ context and other background information. This paper summarizes the different social media violent categories and existing methods to detect the violent content.Keywords: Machine learning, natural language processing, violence, social media, convolution neural networ

Predicting Tie Strength between Facebook Friends to Improve Accuracy in Travel Recommendation Systems

People rely on their trusted circle of friends for advice and recommendations on everything from travel destinations to purchase decisions. With the extensive use of social networks these relationships are now taken to an electronic platform, where they manifest as likes, comments, wall posts, etc., on social media networks. This paper explores the novel idea that such user relationships can be extracted to significantly improve the accuracy of commercial recommendation systems by identifying otherwise hidden relationships between users. A multiple linear regression based model capable of extracting such user relationships and their corresponding strength efficiently is introduced under this research and the above hypothesis is tested by integrating the predictive model to an existing social media based travel recommendation system. Finally, experimental results of the proposed model are produced, proving the capability of the model in achieving a significant increase in accuracy in travel recommendations, affirming the considered hypothesis

Social Business Intelligence: a Literature Review and Research Agenda

The domains of Business Intelligence (BI) and social media have meanwhile become significant research fields. While BI aims at supporting an organization’s decisions by providing relevant analytical data, social media is an emerging source of personal and individual knowledge, opinion, and attitudes of stakeholders. For a while, a convergence of the two domains can be observed in real-world implementations and research, resulting in concepts like social BI. Many research questions still remain open – or even worse – are not yet formulated. Therefore, the paper aims at articulating a research agenda for social BI. By means of a literature review we systematically explored previous work and developed a framework. It contrasts social media characteristics with BI design areas and is used to derive the social BI research agenda. Our results show that the integration of social media (data) into a BI system has impact on almost all BI design objects

Facebook Marketing Intelligence

Facebook marketing is becoming an increasingly important tool for companies to influence consumer decision-making. However, there is currently little empirical knowledge about the extent of influence of Facebook marketing on the decision-making process of consumers. This study contributes to these gaps in the literature and investigates the influence of Facebook marketing activities on the decision-making process of consumers. The theory revealed four Facebook marketing activities that affected the first two phases of the decision-making process. These Facebook marketing activities were advertisements, recommend/share, likes and reviews. Whether they actually had an impact has been tested with the help of survey among 112 respondents. The results of the regression analysis showed that all four Facebook marketing activities had a positive influence on the decision-making process