Application of Common Sense Computing for the Development of a Novel Knowledge-Based Opinion Mining Engine

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience

Application of Common Sense Computing for the Development of a Novel Knowledge-Based Opinion Mining Engine

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience

Red Bones and Earth Mothers: A Contemporary Exploration of Colorism and its Perception Among African American Female Adolescents

Research on colorism continues to gain momentum across several disciplines. However, while varied studies have explored the social phenomenon among adult populations, especially those of African ancestry, few have systematically investigated the extent to which African American youth are exposed to or endorse hierarchical perceptions of skin color. The current study addresses this void in colorism literature. Employing a grounded theory approach, the present investigation examines African American female adolescents’ perceptions of skin color, aiming specifically to understand the sociocultural factors that underpin and contribute to colorist socializations as well as sources of skin color messages. Five focus groups and nine interviews were conducted with 30 African American girls ranging in age from 12-16. Participants were recruited from local Boys and Girls clubs, neighborhood centers, and nonprofit organizations. Participants were asked such a priori based questions as: 1) What do people think about light skin Black girls? 2) What do people think about dark skin Black girls? 3) What messages about skin color do you hear from Rap music? and 4) Do Black men and boys prefer girls of certain skin colors. Constant comparison data analysis and coding revealed African Americans girls are, in fact, exposed to and endorse hierarchical perceptions of skin color, the central phenomenon Three core categories related to the central phenomenon emerged: 1) sources of skin color messages, e.g. family and rap music 2) skin color messages, e.g. skin color governs social standing, physical attributes, and personality/behavioral traits and 3) effects of skin color messages, e.g. mate preferences, desires to change one’s appearance, and within-race division. From these three core categories emerged seven subcategories and themes that offer additional information and insight into the central phenomenon. Findings from this study indicate African American young females are significantly influenced by skin color preferences, and thus may stand to gain from the development of curricula or programs designed to counter colorist stereotypes, reduce the effects of skin color biases, and promote a greater sense of self-satisfaction and wellbeing

Sentiment analysis in retail: the case of Parfois facebook page

The way that consumers are interacting with brands is changing, and in Retail it is no different. With the growth of internet usage and with all the social networks that we interact with, social media is gaining more and more relevance and importance. This research extracted 1.845 posts, 8.256 comments and more than 500.000 reactions from Parfois Facebook page. The comments were translated to English due to having comments made in several different languages, modelled and finally made the sentiment analysis. This analysis was made concerning the post dates, the reasons of the post and the products associated in the post. It was used decision tree algorithms to predict sentiments, so it can be predicted the sentiment when making a new post. With the Sentiment Analysis from Social Media, Parfois can gain understanding about their own brand, from the marketing department through to the buying or even design departments. Using Social Media analysis together with Business Intelligence, can help Parfois decision makers gain competitive advantage regarding their competitors or even improve their products.A maneira como os consumidores interagem com as marcas está a mudar, e no retalho não é diferente. Com o aumento do uso da internet e com todas as redes sociais que interagimos, as redes sociais ganham mais relevância e importância. Esta pesquisa extraiu 1.845 posts, 8.256 comentários e mais de 500.000 reações da página de Facebook da Parfois. Os comentários foram traduzidos para o inglês devido ao fato de haver comentários feitos em várias línguas diferentes, modelados e finalmente feita a análise de sentimentos. Esta análise foi feita em relação às datas das publicações, os motivos do post, os produtos associados ao post. Foram utilizados algoritmos de árvores de decisão para prever sentimentos para que se possa prever o sentimento ao fazer um novo post. Com a analise de sentimentos das redes sociais, a Parfois pode entender melhor a sua própria marca, desde o departamento de marketing até ao departamento de compras ou mesmo o departamento de design. Usar a análise de sentimentos das redes sociais junto com o Business Intelligence organizacional, pode ajudar os decisores da Parfois a ganhar vantagem competitiva em relação aos concorrentes ou mesmo a melhorar seus produtos

The impact of online reviews on consumer evaluations and decision making: an analysis of review volume and user-generated photos

This thesis investigates the impact of online social influence on consumer behaviour, specifically within the context of online reviews. It examines how review volume and user-generated photos affect consumer evaluations and decision-making. In Chapter 2, I introduce a novel phenomenon, the N-effect, which explores how opinion volume influences the content of online evaluations. I find that as the number of opinions increases, the content becomes more emotional and less analytical. In Chapter 3, I investigate the role of user-generated photos in shaping purchase intentions. This research demonstrates that photos can enhance review helpfulness, even when they lack diagnostic information. This effect is driven by the confidence signalled by the reviewer when posting a review with a photo, which is later assimilated by readers, leading to increased perceived helpfulness and purchase likelihood. This thesis makes several theoretical and practical contributions to the literature on human interaction with technology. Theoretically, it expands our understanding of online social influence by examining the dynamics of online opinion expression and content. I contribute to the literature on group size by demonstrating how responsibility may be lost in online contexts. Furthermore, the findings provide insights into the social influence of photos on viewers and the role of pseudo-evidence in shaping beliefs and attitudes. From a practical standpoint, this research offers valuable insights for online platform managers and marketers on interpreting and using consumer-written reviews. Overall, this thesis contributes to the existing literature on online social influence and provides insights for businesses to improve communication and interpretation with consumers by better understanding and leveraging online reviews and opinions.Open Acces