Topic-dependent sentiment analysis of financial blogs

While most work in sentiment analysis in the financial domain has focused on the use of content from traditional finance news, in this work we concentrate on more subjective sources of information, blogs. We aim to automatically determine the sentiment of financial bloggers towards companies and their stocks. To do this we develop a corpus of financial blogs, annotated with polarity of sentiment with respect to a number of companies. We conduct an analysis of the annotated corpus, from which we show there is a significant level of topic shift within this collection, and also illustrate the difficulty that human annotators have when annotating certain sentiment categories. To deal with the problem of topic shift within blog articles, we propose text extraction techniques to create topic-specific sub-documents, which we use to train a sentiment classifier. We show that such approaches provide a substantial improvement over full documentclassification and that word-based approaches perform better than sentence-based or paragraph-based approaches

Analysis of the Geosocial Landscape in the City of Toronto

Microblogging on geosocial platforms is a popular form of online communication where users post information about their daily lives and challenges. Since the launch of Twitter in 2006, information sharing through social media has become a largely unused data repository. Tweets often convey content about the users sentiment as it is happening. As such, Tweets can be viewed as a proxy of public mood. In this thesis, I performed a sentiment analysis of all public geo-located Tweets posted by a variety of Twitter users between September 2013 and October 2014. Each Tweet was processed through a custom algorithm to extract 8 different emotions: Anger, Confusion, Disgust, Fear, Happiness, Sadness, Shame, and Surprise. I then created an emotional landscape to display variance in emotion across the city of Toronto. The emotional landscape presented interesting emotional polarity change between the core and the periphery of the city. Neighbourhood profiles were then used to compare the emotional differences resource access could individual’s ability to cope and mediate stress. I found that individuals living within close proximity to greenspace expressed increased levels of positivity though they have decreased access to built resources. I also found that individuals within Neighbourhood Improvement Areas experienced an increased risk of negativity. I believe large-scale analyses of public sentiment can provide valuable information for further analysis of resource use in an effort to reduce negative health effects long term

A happiness degree predictor using the conceptual data structure for deep learning architectures

[EN] Background and Objective: Happiness is a universal fundamental human goal. Since the emergence of Positive Psychology, a major focus in psychological research has been to study the role of certain factors in the prediction of happiness. The conventional methodologies are based on linear relationships, such as the commonly used Multivariate Linear Regression (MLR), which may suffer from the lack of representative capacity to the varied psychological features. Using Deep Neural Networks (DNN), we define a Happiness Degree Predictor (H-DP) based on the answers to five psychometric standardized questionnaires. Methods: A Data-Structure driven architecture for DNNs (D-SDNN) is proposed for defining an HDP in which the network architecture enables the conceptual interpretation of psychological factors associated with happiness. Four different neural network configurations have been tested, varying the number of neurons and the presence or absence of bias in the hidden layers. Two metrics for evaluating the influence of conceptual dimensions have been defined and computed: one quantifies the influence weight of the conceptual dimension in absolute terms and the other one pinpoints the direction (positive or negative) of the influence. Materials: A cross-sectional survey targeting the non-institutionalized adult population residing in Spain was completed by 823 cases. The total of 111 elements of the survey are grouped by socio-demographic data and by five psychometric scales (Brief COPE Inventory, EPQR-A, GHQ-28, MOS-SSS, and SDHS) measuring several psychological factors acting one as the outcome (SDHS) and the four others as predictors. Results: Our D-SDNN approach provided a better outcome (MSE: 1.46 · 10^-2 ) than MLR (MSE: 2.30 · 10^-2 ), hence improving by 37% the predictive accuracy, and allowing to simulate the conceptual structure. Conclusions: We observe a better performance of Deep Neural Networks (DNN) with respect to traditional methodologies. This demonstrates its capability to capture the conceptual structure for predicting happiness degrees through psychological variables assessed by standardized questionnaires. It also permits to estimate the influence of each factor on the outcome without assuming a linear relationship.Perez-Benito, FJ.; Villacampa-Fernandez, P.; Conejero, JA.; Garcia-Gomez, JM.; Navarro-Pardo, E. (2019). A happiness degree predictor using the conceptual data structure for deep learning architectures. Computer Methods and Programs in Biomedicine. 168:59-68. https://doi.org/10.1016/j.cmpb.2017.11.004S596816

Tracking Sentiments toward Fat Acceptance over a Decade on Twitter

The fat acceptance (FA) movement aims to counteract weight stigma and discrimination against individuals who are overweight/obese. We developed a supervised neural network model to classify sentiment toward the FA movement in tweets and identify links between FA sentiment and various Twitter user characteristics. We collected any tweet containing either “fat acceptance” or “#fatacceptance” from 2010–2019 and obtained 48,974 unique tweets. We independently labeled 2000 of them and implemented/trained an Average stochastic gradient descent Weight-Dropped Long Short-Term Memory (AWD-LSTM) neural network that incorporates transfer learning from language modeling to automatically identify each tweet’s stance toward the FA movement. Our model achieved nearly 80% average precision and recall in classifying “supporting” and “opposing” tweets. Applying this model to the complete dataset, we observed that the majority of tweets at the beginning of the last decade supported FA, but sentiment trended downward until 2016, when support was at its lowest. Overall, public sentiment is negative across Twitter. Users who tweet more about FA or use FA-related hashtags are more supportive than general users. Our findings reveal both challenges to and strengths of the modern FA movement, with implications for those who wish to reduce societal weight stigma

Fake Content Detection in the Information Exponential Spreading Era

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementRecent years brought an information access democratization, allowing people to access a huge amount of information and the ability to share it, in a way that it can easily reach millions of people in a very short time. This allows to have right and wrong uses of this capabilities, that in some cases can be used to spread malicious content to achieve some sort of goal. Several studies have been made regarding text mining and sentiment analysis, aiming to spot fake information and avoid misinformation spreading. The trustworthiness and veracity of the information that is accessible to people is getting increasingly important, and in some cases critical, and can be seen has a huge challenge for the current digital era. This problem might be addressed with the help of science and technology. One question that we can do to ourselves is: How do we guarantee that there is a correct use of information, and that people can trust in the veracity of it? Using mathematics and statistics, combined with machine learning classification and predictive algorithms, using the current computation power of information systems, can help minimize the problem, or at least spot the potential fake information. One suggests developing a research work that aims to reach a model for the prediction of a given text content is trustworthy. The results were promising reaching a predicting model with good performance

User Review-Based Change File Localization for Mobile Applications

In the current mobile app development, novel and emerging DevOps practices (e.g., Continuous Delivery, Integration, and user feedback analysis) and tools are becoming more widespread. For instance, the integration of user feedback (provided in the form of user reviews) in the software release cycle represents a valuable asset for the maintenance and evolution of mobile apps. To fully make use of these assets, it is highly desirable for developers to establish semantic links between the user reviews and the software artefacts to be changed (e.g., source code and documentation), and thus to localize the potential files to change for addressing the user feedback. In this paper, we propose RISING (Review Integration via claSsification, clusterIng, and linkiNG), an automated approach to support the continuous integration of user feedback via classification, clustering, and linking of user reviews. RISING leverages domain-specific constraint information and semi-supervised learning to group user reviews into multiple fine-grained clusters concerning similar users' requests. Then, by combining the textual information from both commit messages and source code, it automatically localizes potential change files to accommodate the users' requests. Our empirical studies demonstrate that the proposed approach outperforms the state-of-the-art baseline work in terms of clustering and localization accuracy, and thus produces more reliable results.Comment: 15 pages, 3 figures, 8 table