Sentiment Analysis in Social Streams

In this chapter we review and discuss the state of the art on sentiment analysis in social streams –such as web forums, micro-blogging systems, and so- cial networks–, aiming to clarify how user opinions, affective states, and intended emotional effects are extracted from user generated content, how they are modeled, and how they could be finally exploited. We explain why sentiment analysis tasks are more difficult for social streams than for other textual sources, and entail going beyond classic text-based opinion mining techniques. We show, for example, that social streams may use vocabularies and expressions that exist outside the main- stream of standard, formal languages, and may reflect complex dynamics in the opinions and sentiments expressed by individuals and communities

Sentiment Analysis in Social Streams

In this chapter, we review and discuss the state of the art on sentiment analysis in social streams—such as web forums, microblogging systems, and social networks, aiming to clarify how user opinions, affective states, and intended emo tional effects are extracted from user generated content, how they are modeled, and howthey could be finally exploited.We explainwhy sentiment analysistasks aremore difficult for social streams than for other textual sources, and entail going beyond classic text-based opinion mining techniques. We show, for example, that social streams may use vocabularies and expressions that exist outside the mainstream of standard, formal languages, and may reflect complex dynamics in the opinions and sentiments expressed by individuals and communities

Studying Real-Time Audience Responses to Political Messages: A New Research Agenda

Real-time response methods, which were developed by media and communication researchers as early as the 1940s, have significant potential for understanding media audiences today. However, this potential is not realized fully by current methods such as “the worm,” which are limited to collecting positive and negative responses and fail to examine why audience members respond as they do. This article advocates a new research agenda for understanding how audiences respond to political messages through real-time response methods. Instead of measuring preferences, we suggest that realtime response methods should focus on people’s sense of whether their democratic capabilities are advanced—an approach that would provide a more critical as well as a more nuanced understanding of how audiences respond to political communication. We describe an innovative Web-based app our team has designed to capture audience responses to political messages, and we outline some key questions we hope to address in future research

Sentiment Lexicon Adaptation with Context and Semantics for the Social Web

Sentiment analysis over social streams offers governments and organisations a fast and effective way to monitor the publics' feelings towards policies, brands, business, etc. General purpose sentiment lexicons have been used to compute sentiment from social streams, since they are simple and effective. They calculate the overall sentiment of texts by using a general collection of words, with predetermined sentiment orientation and strength. However, words' sentiment often vary with the contexts in which they appear, and new words might be encountered that are not covered by the lexicon, particularly in social media environments where content emerges and changes rapidly and constantly. In this paper, we propose a lexicon adaptation approach that uses contextual as well as semantic information extracted from DBPedia to update the words' weighted sentiment orientations and to add new words to the lexicon. We evaluate our approach on three different Twitter datasets, and show that enriching the lexicon with contextual and semantic information improves sentiment computation by 3.4% in average accuracy, and by 2.8% in average F1 measure

SELEKSI FITUR INFORMATION GAIN DAN ALGORITMA NAÏVE BAYES UNTUK REVIEW OPINI KONSUMEN

The growth of internet users in Indonesia is increasing, this is in line with online shopping habits or often referred to as e-commerce which continues to increase. Various things are done by e-commerce companies to maintain customer loyalty, one of which is through product evaluation using consumer opinion reviews. The number of reviews that are too many will be biased, so it is necessary to do a classification method that will help e-commerce companies to find out the extent of their customer loyalty. Consumer review becomes something important because all assessments of the products they buy are all in the review column. In this research, a consumer review is carried out using the Naive Bayes classification method and to improve the accuracy of attributes using the Information Gain feature selection and using the Select by Weight operator which will display the best attributes of the pre processing process. The review data set is taken from consumers' comments on Google Play. The results of this study are classifying consumer reviews into positive reviews and negative reviews with Cross Validation using 10 fold, the accuracy of the Naive Bayes method is 78.4% using the Information Gain feature selection method, the accuracy increases to 81.2

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

Semi-supervised learning and fairness-aware learning under class imbalance

With the advent of Web 2.0 and the rapid technological advances, there is a plethora of data in every field; however, more data does not necessarily imply more information, rather the quality of data (veracity aspect) plays a key role. Data quality is a major issue, since machine learning algorithms are solely based on historical data to derive novel hypotheses. Data may contain noise, outliers, missing values and/or class labels, and skewed data distributions. The latter case, the so-called class-imbalance problem, is quite old and still affects dramatically machine learning algorithms. Class-imbalance causes classification models to learn effectively one particular class (majority) while ignoring other classes (minority). In extend to this issue, machine learning models that are applied in domains of high societal impact have become biased towards groups of people or individuals who are not well represented within the data. Direct and indirect discriminatory behavior is prohibited by international laws; thus, there is an urgency of mitigating discriminatory outcomes from machine learning algorithms. In this thesis, we address the aforementioned issues and propose methods that tackle class imbalance, and mitigate discriminatory outcomes in machine learning algorithms. As part of this thesis, we make the following contributions: • Tackling class-imbalance in semi-supervised learning – The class-imbalance problem is very often encountered in classification. There is a variety of methods that tackle this problem; however, there is a lack of methods that deal with class-imbalance in the semi-supervised learning. We address this problem by employing data augmentation in semi-supervised learning process in order to equalize class distributions. We show that semi-supervised learning coupled with data augmentation methods can overcome class-imbalance propagation and significantly outperform the standard semi-supervised annotation process. • Mitigating unfairness in supervised models – Fairness in supervised learning has received a lot of attention over the last years. A growing body of pre-, in- and postprocessing approaches has been proposed to mitigate algorithmic bias; however, these methods consider error rate as the performance measure of the machine learning algorithm, which causes high error rates on the under-represented class. To deal with this problem, we propose approaches that operate in pre-, in- and post-processing layers while accounting for all classes. Our proposed methods outperform state-of-the-art methods in terms of performance while being able to mitigate unfair outcomes

A social media analytics framework for decision-making in citizen relationship management

Globally social media has shown unprecedented levels of adoption and Social Media Analytics (SMA) is a rapidly growing topic. For governments, SMA holds the promise of providing tools and frameworks to collect, monitor, analyse and visualise social media data, usually driven by specific requirements from a target application. However, social media data is noisy and unstructured, and organisations struggle to extract knowledge from this data, and convert it into actual intelligence. This study argues that SMA can support intelligent decision-making for Citizen Relationship Management (CzRM). CzRM is a growing effort of governments around the world to strive to respond rapidly to their citizens by fostering a closer relationship thereby creating more effective and efficient service delivery. However, there is a little evidence in literature on empirical studies of any existing decision-making framework for CzRM and SMA adoption. In particular, there is a gap with regards incorporating SMA into decision-making for CzRM of governments, particularly in developing countries like South Africa. The aim of this study was to develop a framework that provides guidelines, including methods and tools, incorporating SMA into decision-making for CzRM in the Gauteng Provincial Government (GPG) and the Free State Provincial Government (FSPG) of South Africa. A Systematic Literature Review (SLR) and conceptual analysis method was conducted to design the Social Media Analytics Framework for Decision-making in the context of CzRM (the SMAF). The findings from the literature review revealed several benefits and challenges with SMA, in particular the shortage of skills, guidelines, methods and tools for SMA. These challenges were used to draft guidelines that were included in the framework, which consists of five components that can be used to derive intelligent information from SMA. The pragmatic philosophy and a case study design was used to generate an in-depth, multifaceted understanding of the underlying problems in the case of the GPG and the FSPG. The German North-West Metropolitan region was used as a third case study to provide a more global perspective and a case of a developed country in terms of Gross Domestic Product. The scope of the study was limited to social media posts by provincial citizens related to CzRM and service delivery. Both formative and summative evaluations of the proposed theoretical framework were conducted. The formative evaluation was conducted v | Page as an Expert Review to receive feedback of the framework from the experts in the field of Computer Science and Information Systems. The findings validated the framework and some minor improvements were made based on the experts’ recommendations. Focus Group Discussions (FGDs) with participants from government managers and decision makers in the three cases were conducted. Case documents for the three cases were collected and reviewed. All collected data was analysed using the Qualitative Content Analysis (QCA) method and common categories and themes were identified. Summative evaluations were conducted in the form of a Field Study, which consisted of an analysis of Twitter data from the three cases, and a closing FGD with Business Intelligence (BI) experts at the primary case of the e-Government department of the GPG. The findings revealed that SMA has been adopted in all three cases; however, while their strategies are comprehensive their implementations are very much in their early stages. The findings also highlighted the status of SMA in government and some potential gaps and areas for implementing the framework.Thesis (PhD) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 202

Semantic Sentiment Analysis in Social Streams

Microblogs and social media platforms are now considered among the most popular forms of online communication. Through a platform like Twitter, much information reflecting people’s opinions and attitudes is published and shared among users on a daily basis. This has recently brought great opportunities to companies interested in tracking and monitoring the reputation of their brands and businesses, and to policy makers and politicians to support their assessment of public opinions about their policies or political issues. A wide range of approaches to sentiment analysis on social media, have been recently built. Most of these approaches rely mainly on the presence of affect words or syntactic structures that explicitly and unambiguously reflect sentiment. However, these approaches are semantically weak, that is, they do not account for the semantics of words when detecting their sentiment in text. In order to address this problem, the author investigates the role of word semantics in sentiment analysis of microblogs. Specifically, Twitter is used as a case study of microblogging platforms to investigate whether capturing the sentiment of words with respect to their semantics leads to more accurate sentiment analysis models on Twitter. To this end, the author proposes several approaches in this book for extracting and incorporating two types of word semantics for sentiment analysis: contextual semantics (i.e., semantics captured from words’ co-occurrences) and conceptual semantics (i.e., semantics extracted from external knowledge sources). Experiments are conducted with both types of semantics by assessing their impact in three popular sentiment analysis tasks on Twitter; entity-level sentiment analysis, tweet-level sentiment analysis and context-sensitive sentiment lexicon adaptation. The findings from this body of work demonstrate the value of using semantics in sentiment analysis on Twitter. The proposed approaches, which consider word semantics for sentiment analysis at both entity and tweet levels, surpass non-semantic approaches in most evaluation scenarios. This book will be of interest to students, researchers and practitioners in the semantic sentiment analysis field