A Crowdsourcing Based Framework for Sentiment Analysis: A Product Reputation

As social networking has spread, people started sharing their personal opinions and thoughts widely via these online platforms. The resulting vast valuable data represent a rich source for companies to deduct their products’ reputation from both social media and crowds’ judgments. To exploit this wealth of data, a framework was proposed to collect opinions and rating scores respectively from social media and crowdsourcing platform to perform sentiment analysis, provide insights about a product and give consumers’ tendencies. During the analysis process, a consumer category (strict) is excluded from the process of reaching a majority consensus. To overcome this, a fuzzy clustering is used to compute consumers’ credibility. The key novelty of our approach is the new layer of validity check using a crowdsourcing component that ensures that the results obtained from social media are supported by opinions extracted directly from real-life consumers. Finally, experiments are carried out to validate this model (Twitter and Facebook were used as data sources). The obtained results show that this approach is more efficient and accurate than existing solutions thanks to our two-layer validity check design

A credibility and classification-based approach for opinion analysis in social networks

© Springer International Publishing Switzerland 2016. There is an ongoing interest in examining users’ experiences made available through social media. Unfortunately these experiences like reviews on products and/or services are sometimes conflicting and thus, do not help develop a concise opinion on these products and/or services. This paper presents a multi-stage approach that extracts and consolidates reviews after addressing specific issues such as user multiidentity and user limited credibility. A system along with a set of experiments demonstrate the feasibility of the approach

Impact of credibility on opinion analysis in social media

© 2018 IOS Press. All rights reserved. In conjunction with the rapid growth and adoption of social media, people are more and more willing to share their personal experiences and opinions about products and/or services with the community. Opinions could be the basis of developing systems that would advise future users on how to proceed with any purchase without risking any disappointment. Unfortunately, opinions are not always genuine due to for instance, biased users as well as mixed feedback coming from the same users (i.e., multi-identity). This paper presents an approach for opinion analysis using credibility as a decisive criterion for supporting future users make sound decisions. The effectiveness of this approach has been tested using opinions posted on Twitter

The aesthetics and politics of ‘reading together’ Moroccan novels in Arabic and French

This paper attempts to break down the common practices of reading multilingual Moroccan novels, particularly Moroccan postcolonial novels in Arabic and French. I argue that dominant reading practices are based on binary oppositions marked by a reductionist understanding of language and cultural politics in Morocco. They place the Moroccan novel in Arabic and French in independent traditions with the presupposition that they have no impact on each other, thereby reifying each tradition. They also ignore the similar historical, social and cultural context from which these novels emerge, and tend to reinforce the marginalisation of the Moroccan novel within hegemonic single-language literary systems such as the Francophone or Arabic literary traditions. I advocate ‘reading together’ – or an entangled comparative reading of – postcolonial Moroccan novels in Arabic and French, a reading that privileges the specificity of the literary traditions in Morocco rather than language categorisation, and that considers their mutual historical, cultural, geographical, political, and aesthetic interweaving and implications

Social intelligence framework: Extracting and analyzing opinions for social CRM

International audienc

A MULTI-AGENT BASED SOCIAL CRM FRAMEWORK FOR EXTRACTING AND ANALYSING OPINIONS

Social media provide a wide space for people from around the world to communicate, share knowledge and personal experiences. They increasingly become an important data source for opinion mining and sentiment analysis, thanks to shared comments and reviews about products and services. And companies are showing a growing interest to harness their potential, in order to support setting up marketing strategies. Despite the importance of sentiment analysis in decision making, there is a lack of social intelligence integration at the level of customer relationship management systems. Thus, social customer relationship management (SCRM) systems have become an interesting research area. However, they need deep analytic techniques to transform the large amount of data “Big Data” into actionable insights. Such systems also require an advanced modelling and data processing methods, and must consider the emerging paradigm related to proactive systems. In this paper, we propose an agent based social framework that extracts and consolidates the reviews expressed via social media, in order to help enterprises know more about customers’ opinions toward a particular product or service. To illustrate our approach, we present the case study of Twitter reviews that we use to extract opinions and sentiment about a set of products using SentiGem API. Data extraction, analysis and storage are performed using a framework based on Hadoop MapReduce and HBase

A product reputation framework based on social multimedia content

International audienc

Multi-agent framework for social CRM: Extracting and analyzing opinions

International audienc