A Machine Learning Approach For Opinion Holder Extraction In Arabic Language

Opinion mining aims at extracting useful subjective information from reliable amounts of text. Opinion mining holder recognition is a task that has not been considered yet in Arabic Language. This task essentially requires deep understanding of clauses structures. Unfortunately, the lack of a robust, publicly available, Arabic parser further complicates the research. This paper presents a leading research for the opinion holder extraction in Arabic news independent from any lexical parsers. We investigate constructing a comprehensive feature set to compensate the lack of parsing structural outcomes. The proposed feature set is tuned from English previous works coupled with our proposed semantic field and named entities features. Our feature analysis is based on Conditional Random Fields (CRF) and semi-supervised pattern recognition techniques. Different research models are evaluated via cross-validation experiments achieving 54.03 F-measure. We publicly release our own research outcome corpus and lexicon for opinion mining community to encourage further research

Assessing Public Opinions Through Web 2.0: A Case Study on Wal-Mart

The recent advancement of Web 2.0 enables people to exchange their opinions on a variety of topics. Among these discussions, the opinions of employees, customers, and investors are of great interest to companies. Insight into such perspectives can help managers make better decisions on business policies and strategy. However, assessing online opinions is a nontrivial task. The high volume of messages, casual writing style, and the significant amount of noise require the application of sophisticated text mining techniques to digest the data. Previous research has successfully applied sentiment analysis to assess online opinions on specific items and topics. In this research, we propose the integration of topic analysis with sentiment analysis methods to assess the public opinions expressed in forums with diverse topics of discussion. Using a Wal- Mart-related Web forum as an example, we found that combining the two types of analysis can provide us with improved ability to assess public opinions on a company. Through further analysis on one cluster of discussions, several abnormal traffic and sentiment patterns were identified related to Wal-Mart events. The case study validates the propose framework as an IT artifact to assess online public opinion on companies of interest. Our research promotes further efforts to combine topic and sentiment analysis techniques in online research supporting business decision making

Brand Positioning Map and Analysis Using Web Scraping and Advertisement Analysis

There’s a significant increase in online consumer forums. When customers set out to buy a product they use these forums to form an opinion. Our research focuses on comparing Brand positioning maps based on consumer reviews. We also analyse the impact of advertisements and expert reviews. Our goal is to show that combining consumer reviews with ads and electronic media will help us analyze the effectiveness of advertising on brand positioning maps. This approach shall also help us in making association graphs for a brand using words of perception/opinion associated with that brand/product. Which may in turn assist companies in improving the focus of their advertisements to persuade the required set of crowd and influence the public perception

TRANSFORMING GOVERNMENT AGENCIES’ APPROACH TO EPARTICIPATION THROUGH EFFICIENT EXPLOITATION OF SOCIAL MEDIA

Government agencies are making considerable investments for exploiting the capabilities offered by ICT, and especially the Internet, to increase citizens’ engagement in their decision and policy making processes. However, this first generation of e-participation has been characterised by limited usage of the ‘official’ e-consultation spaces of government agencies by the citizens. The emergence of Web 2.0 social media offers big opportunities for overcoming this problem, and proceeding to a second generation of broader, deeper and more advanced e-participation. This paper presents a methodology for the efficient exploitation of Web 2.0 social media by government agencies in order to broaden and enhance e-participation. It is based on a central platform which enables posting content and deploying micro web applications (‘Policy Gadgets’-Padgets) to multiple popular Web 2.0 social media, and also collecting users’ interactions with them (e.g. views, comments, ratings) in an efficient manner using their application programming interfaces (API). These interactions’ data undergo various levels of processing, such as calculation of useful analytics, opinion mining and simulation modelling, in order to provide effective support to public decision and policy makers. The proposed methodology allows government agencies to adopt advanced and highly effective ‘hybrid’ e-participation approaches

Stakeholders’ Use of Microblogging to Engage in Emotion Strategies During a Crisis

The rise of microblogging has drastically transformed the ways in which people become aware of, talk about, experience, and respond to crises. Microblogging allows for multiple stakeholders to express and manage emotions that a crisis may trigger. This research examines how multiple stakeholders engage in emotion strategies through microblogging over the course of a crisis. Relying upon and extending emerging literatures on crisis management and the psycho-sociology of emotions, we propose the concept of emotion strategies to explore and elaborate upon the different uses of emotions and their crucial importance in a crisis context. We examine how microblogging features and affordances might enable and shape the emotion strategies of various stakeholders involved in a crisis. We outline the details of an ongoing investigation in the context of the 2010 Gulf of Mexico oil spill and provide illustrative insights. We conclude by highlighting future steps in this research and expected contributions

An Account of Opinion Implicatures

While previous sentiment analysis research has concentrated on the interpretation of explicitly stated opinions and attitudes, this work initiates the computational study of a type of opinion implicature (i.e., opinion-oriented inference) in text. This paper described a rule-based framework for representing and analyzing opinion implicatures which we hope will contribute to deeper automatic interpretation of subjective language. In the course of understanding implicatures, the system recognizes implicit sentiments (and beliefs) toward various events and entities in the sentence, often attributed to different sources (holders) and of mixed polarities; thus, it produces a richer interpretation than is typical in opinion analysis.Comment: 50 Pages. Submitted to the journal, Language Resources and Evaluatio