Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline

Opinion mining and sentiment analysis in marketing communications: a science mapping analysis in Web of Science (1998–2018)

Opinion mining and sentiment analysis has become ubiquitous in our society, with applications in online searching, computer vision, image understanding, artificial intelligence and marketing communications (MarCom). Within this context, opinion mining and sentiment analysis in marketing communications (OMSAMC) has a strong role in the development of the field by allowing us to understand whether people are satisfied or dissatisfied with our service or product in order to subsequently analyze the strengths and weaknesses of those consumer experiences. To the best of our knowledge, there is no science mapping analysis covering the research about opinion mining and sentiment analysis in the MarCom ecosystem. In this study, we perform a science mapping analysis on the OMSAMC research, in order to provide an overview of the scientific work during the last two decades in this interdisciplinary area and to show trends that could be the basis for future developments in the field. This study was carried out using VOSviewer, CitNetExplorer and InCites based on results from Web of Science (WoS). The results of this analysis show the evolution of the field, by highlighting the most notable authors, institutions, keywords, publications, countries, categories and journals.The research was funded by Programa Operativo FEDER Andalucía 2014‐2020, grant number “La reputación de las organizaciones en una sociedad digital. Elaboración de una Plataforma Inteligente para la Localización, Identificación y Clasificación de Influenciadores en los Medios Sociales Digitales (UMA18‐ FEDERJA‐148)” and The APC was funded by the same research gran

A framework for applying natural language processing in digital health interventions

BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts

Predicting online product sales via online reviews, sentiments, and promotion strategies

Purpose – The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews can help predict product sales. Design/methodology/approach – The authors designed a big data architecture and deployed Node.js agents for scraping the Amazon.com pages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales. Findings – This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with sentiments and discounts are more important than the individual predictors of discounts, sentiments or online volume. Originality/value – This study designed big data architecture, in combination with sentimental and neural network analysis that can facilitate future business research for predicting product sales in an online environment. This study also employed a predictive analytic approach (e.g. neural network) to examine the variables, and this approach is useful for future data analysis in a big data environment where prediction can have more practical implications than significance testing. This study also examined the interplay between online reviews, sentiments and promotional strategies, which up to now have mostly been examined individually in previous studies

Predicting Consumers’ Brand Sentiment Using Text Analysis on Reddit

With the emergence of data privacy regulations around the world (e.g. GDPR, CCPA), practitioners of Internet marketing, the largest digital marketing channel, face the trade-off between user data protection and advertisement targeting accuracy due to their current reliance on PII-related social media analytics. To address this challenge, this research proposes a predictive model for consumers’ brand sentiment based entirely on textual data from Reddit, i.e. fully compliant with current data privacy regulations. This author uses natural language processing techniques to process all post and comment data from the r/gadgets subreddit community in 2018 – extracting frequently-discussed brands and products through named entity recognition, as well as generating brand sentiment labels for active users in r/gadgets through sentiment analysis. This research then uses four supervised learning classifiers to predict brand sentiments for four brand clusters (Apple, Samsung, Microsoft and Google) based on the self-identified characteristics of Reddit users. Across all four brand clusters, the predictive model proposed by this research achieved a ROC AUC score above 0.7 (three out of the four above 0.8). This research thus shows the predictive power of self-identified user characteristics on brand sentiments and offers a non-PII-required consumer targeting model for digital marketing practitioners

Twitter Sentiment Analysis: An Examination of Cybersecurity Attitudes and Behavior

This exploratory study examines the cybersecurity attitudes and actual behavior over time using the data collected on the social media microblogging platform, Twitter. We plan to use the sentiment analysis and text mining techniques on original tweets related to cybersecurity collected at two different time periods. Upon completion of this research, we would present the analysis of the relationship between the cybersecurity attitudes and behavior and how behaviors may be shaped by the attitudes. This research work aims to contribute to the extant literature in cybersecurity and endeavors to enhance our understanding of cybersecurity attitude and behavior by validating the proposed research model and hypotheses by using real-time, user-generated, social media data

I Understand What You Are Saying: Leveraging Deep Learning Techniques for Aspect Based Sentiment Analysis

Despite widespread use of online reviews in consumer purchase decision making, the potential value of online reviews in facilitating digital collaboration among product/service providers, consumers, and online retailers remains under explored. One of the significant barriers to realizing the above potential lies in the difficulty of understanding online reviews due to their sheer volume and free-text form. To promote digital collaborations, we investigate aspect based sentiment dynamics of online reviews by proposing a semi-supervised, deep learning facilitated analytical pipeline. This method leverages deep learning techniques for text representation and classification. Additionally, building on previous studies that address aspect extraction and sentiment identification in isolation, we address both aspects and sentiments analyses simultaneously. Further, this study presents a novel perspective to understanding the dynamics of aspect based sentiments by analyzing aspect based sentiment in time series. The findings of this study have significant implications with regards to digital collaborations among consumers, product/service providers and other stakeholders of online reviews