Sentiment Analysis on Financial News and Microblogs

Sentiment analysis is useful for multiple tasks including customer satisfaction metrics, identifying market trends for any industry or products, analyzing reviews from social media comments. This thesis highlights the importance of sentiment analysis, provides a summary of seminal works and different approaches towards sentiment analysis. It aims to address sentiment analysis on financial news and microblogs by classifying textual data from financial news and microblogs as positive or negative. Sentiment analysis is performed by making use of paragraph vectors and logistic regression in this thesis and it aims to compare it with previously performed approaches to performing analysis and help researchers in this field. This approach achieves state of the art results for the dataset used in this research. It also presents an insightful analysis of the results of this approach

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Real-Name Registration Rules and the Fading Digital Anonymity in China

China has implemented comprehensive online real-name registration rules, which require Internet users to disclose their identities. Chinese national law has required most online service providers to implement real-name registration since 2012. This article uses the real-name registration rules to illustrate the supremacy and limitations of the Network Authoritarian Model (NAM), an approach leveraging corporate resources for political surveillance and occasionally adopted by the Chinese party-state. By addressing the evolution of real-name registration rules in China, this article illustrates the party-state’s gradual efforts in both eliminating cyberspace anonymity and etching Chinese characteristics on the architecture of the Internet. Although the Chinese government has faced serious challenges in enforcing the real-name registration policy and current enforcement is far from satisfactory, China is not alone in promoting such a policy. Major Internet companies, including Google, Facebook, and LinkedIn, have expressed similar interests in requiring users to register their real names. Moreover, policymakers in developed and developing countries are exploring similarly themed regulations and China, therefore, may well be in the vanguard of the global movement for online real-name registration. Nonetheless, requiring real-name registration in China has not only created huge costs for Internet companies, but also given rise to fierce controversy associated with free speech, privacy, and law enforcement. In this article, we identify several important legal and policy implications of the Chinese real-name registration policy. We also illustrate the foremost predicament currently faced by the Chinese party-state in enforcing the policy. This analysis argues that China may create a “spillover effect” in jurisdictions outside China as well as in the global Internet architecture

A Domain Oriented LDA Model for Mining Product Defects from Online Customer Reviews

Online reviews provide important demand-side knowledge for product manufacturers to improve product quality. However, discovering and quantifying potential products’ defects from large amounts of online reviews is a nontrivial task. In this paper, we propose a Latent Product Defect Mining model that identifies critical product defects. We define domain-oriented key attributes, such as components and keywords used to describe a defect, and build a novel LDA model to identify and acquire integral information about product defects. We conduct comprehensive evaluations including quantitative and qualitative evaluations to ensure the quality of discovered information. Experimental results show that the proposed model outperforms the standard LDA model, and could find more valuable information. Our research contributes to the extant product quality analytics literature and has significant managerial implications for researchers, policy makers, customers, and practitioners

Sentiment analysis on Twitter

In recent years more and more people have been connecting with Social Networks. One of the most used is Twitter. This huge amount of information is attracting the interest of companies. One reason is that this huge source of information can be used to detect public opinion about their brands and thus improve their business values. In order to transform the information present in the Social Networks into knowledge several steps are required. This project aim to describe them and provide tools that are able to perform this task. The first problem is how to retrieve the data. Several ways are available, each one with its own pros and cons. After that it is necessary to study and define proper queries in order to retrieve the information needed. Once the data is retrieved you may need to filter and explore your data. For this task a Topic Model Algorithm ( LDA ) has been studied and analyzed. LDA has shown positive results when it is tuned in the proper way and it is combined with appropriate visualization techniques. The difference between a Topic Model Algorithm and other Clustering/Segmentation techniques is that Topic Models allows each ”document” ( instance ) to belong to more than one topic ( cluster ). LDA doesn’t natively work well on Twitter due to the very short length of the tweets. An investigation in the literature has revealed a solution to this problem. Another problem that is common in clustering is how to validate the Algorithm and how to choose the proper number of topics ( clusters), for this problem several metrics in the literature have been explored. Afterwards, Sentiment Analysis techniques can be applied in order to measure the opinion of the users . The literature presents several approaches and ways to solving this problem. This work is focused in solving the Polarity Detection task, with three classes , so, classify if a tweet express a positive , a negative or a neutral sentiment. Here reach accurate results can be challenging, due to the messy nature of the twitter posts. Several approaches have been tested and compared. The baseline method tested is the use of sentiment dictionaries, after that , since the real sentiment of the twitter posts is not available, a sample has been manually labeled and several Supervised approaches combined with various Feature Selection/Transformation techniques have been tested. Finally, a totally new experimental approach, inspired from the Soft Labeling technique present in the literature, has been defined and tested. This method try to avoid the costly task to manually label a sample in order to validate a model. In the literature this problem is solved for the two-class problem, so by considering only positive and negative tweets. This work try to extend the soft-labeling approach to the three class problem

A survey of location inference techniques on Twitter

The increasing popularity of the social networking service, Twitter, has made it more involved in day-to-day communications, strengthening social relationships and information dissemination. Conversations on Twitter are now being explored as indicators within early warning systems to alert of imminent natural disasters such as earthquakes and aid prompt emergency responses to crime. Producers are privileged to have limitless access to market perception from consumer comments on social media and microblogs. Targeted advertising can be made more effective based on user profile information such as demography, interests and location. While these applications have proven beneficial, the ability to effectively infer the location of Twitter users has even more immense value. However, accurately identifying where a message originated from or an author’s location remains a challenge, thus essentially driving research in that regard. In this paper, we survey a range of techniques applied to infer the location of Twitter users from inception to state of the art. We find significant improvements over time in the granularity levels and better accuracy with results driven by refinements to algorithms and inclusion of more spatial features

Identification of Online Users' Social Status via Mining User-Generated Data

With the burst of available online user-generated data, identifying online users’ social status via mining user-generated data can play a significant role in many commercial applications, research and policy-making in many domains. Social status refers to the position of a person in relation to others within a society, which is an abstract concept. The actual definition of social status is specific in terms of specific measure indicator. For example, opinion leadership measures individual social status in terms of influence and expertise in an online society, while socioeconomic status characterizes personal real-life social status based on social and economic factors. Compared with traditional survey method which is time-consuming, expensive and sometimes difficult, some efforts have been made to identify specific social status of users based on specific user-generated data using classic machine learning methods. However, in fact, regarding specific social status identification based on specific user-generated data, the specific case has several specific challenges. However, classic machine learning methods in existing works fail to address these challenges, which lead to low identification accuracy. Given the importance of improving identification accuracy, this thesis studies three specific cases on identification of online and offline social status. For each work, this thesis proposes novel effective identification method to address the specific challenges for improving accuracy. The first work aims at identifying users’ online social status in terms of topic-sensitive influence and knowledge authority in social community question answering sites, namely identifying topical opinion leaders who are both influential and expert. Social community question answering (SCQA) site, an innovative community question answering platform, not only offers traditional question answering (QA) services but also integrates an online social network where users can follow each other. Identifying topical opinion leaders in SCQA has become an important research area due to the significant role of topical opinion leaders. However, most previous related work either focus on using knowledge expertise to find experts for improving the quality of answers, or aim at measuring user influence to identify influential ones. In order to identify the true topical opinion leaders, we propose a topical opinion leader identification framework called QALeaderRank which takes account of both topic-sensitive influence and topical knowledge expertise. In the proposed framework, to measure the topic-sensitive influence of each user, we design a novel influence measure algorithm that exploits both the social and QA features of SCQA, taking into account social network structure, topical similarity and knowledge authority. In addition, we propose three topic-relevant metrics to infer the topical expertise of each user. The extensive experiments along with an online user study show that the proposed QALeaderRank achieves significant improvement compared with the state-of-the-art methods. Furthermore, we analyze the topic interest change behaviors of users over time and examine the predictability of user topic interest through experiments. The second work focuses on predicting individual socioeconomic status from mobile phone data. Socioeconomic Status (SES) is an important social and economic aspect widely concerned. Assessing individual SES can assist related organizations in making a variety of policy decisions. Traditional approach suffers from the extremely high cost in collecting large-scale SES-related survey data. With the ubiquity of smart phones, mobile phone data has become a novel data source for predicting individual SES with low cost. However, the task of predicting individual SES on mobile phone data also proposes some new challenges, including sparse individual records, scarce explicit relationships and limited labeled samples, unconcerned in prior work restricted to regional or household-oriented SES prediction. To address these issues, we propose a semi-supervised Hypergraph based Factor Graph Model (HyperFGM) for individual SES prediction. HyperFGM is able to efficiently capture the associations between SES and individual mobile phone records to handle the individual record sparsity. For the scarce explicit relationships, HyperFGM models implicit high-order relationships among users on the hypergraph structure. Besides, HyperFGM explores the limited labeled data and unlabeled data in a semi-supervised way. Experimental results show that HyperFGM greatly outperforms the baseline methods on individual SES prediction with using a set of anonymized real mobile phone data. The third work is to predict social media users’ socioeconomic status based on their social media content, which is useful for related organizations and companies in a range of applications, such as economic and social policy-making. Previous work leverage manually defined textual features and platform-based user level attributes from social media content and feed them into a machine learning based classifier for SES prediction. However, they ignore some important information of social media content, containing the order and the hierarchical structure of social media text as well as the relationships among user level attributes. To this end, we propose a novel coupled social media content representation model for individual SES prediction, which not only utilizes a hierarchical neural network to incorporate the order and the hierarchical structure of social media text but also employs a coupled attribute representation method to take into account intra-coupled and inter-coupled interaction relationships among user level attributes. The experimental results show that the proposed model significantly outperforms other stat-of-the-art models on a real dataset, which validate the efficiency and robustness of the proposed model