Comparison of Sentiment Analysis and User Ratings in Venue Recommendation

Venue recommendation aims to provide users with venues to visit, taking into account historical visits to venues. Many venue recommendation approaches make use of the provided users’ ratings to elicit the users’ preferences on the venues when making recommendations. In fact, many also consider the users’ ratings as the ground truth for assessing their recommendation performance. However, users are often reported to exhibit inconsistent rating behaviour, leading to less accurate preferences information being collected for the recommendation task. To alleviate this problem, we consider instead the use of the sentiment information collected from comments posted by the users on the venues as a surrogate to the users’ ratings. We experiment with various sentiment analysis classifiers, including the recent neural networks-based sentiment analysers, to examine the effectiveness of replacing users’ ratings with sentiment information. We integrate the sentiment information into the widely used matrix factorization and GeoSoCa multi feature-based venue recommendation models, thereby replacing the users’ ratings with the obtained sentiment scores. Our results, using three Yelp Challenge-based datasets, show that it is indeed possible to effectively replace users’ ratings with sentiment scores when state-of-the-art sentiment classifiers are used. Our findings show that the sentiment scores can provide accurate user preferences information, thereby increasing the prediction accuracy. In addition, our results suggest that a simple binary rating with ‘like’ and ‘dislike’ is a sufficient substitute of the current used multi-rating scales for venue recommendation in location-based social networks

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation

Application of Natural Language Processing to Determine User Satisfaction in Public Services

Research on customer satisfaction has increased substantially in recent years. However, the relative importance and relationships between different determinants of satisfaction remains uncertain. Moreover, quantitative studies to date tend to test for significance of pre-determined factors thought to have an influence with no scalable means to identify other causes of user satisfaction. The gaps in knowledge make it difficult to use available knowledge on user preference for public service improvement. Meanwhile, digital technology development has enabled new methods to collect user feedback, for example through online forums where users can comment freely on their experience. New tools are needed to analyze large volumes of such feedback. Use of topic models is proposed as a feasible solution to aggregate open-ended user opinions that can be easily deployed in the public sector. Generated insights can contribute to a more inclusive decision-making process in public service provision. This novel methodological approach is applied to a case of service reviews of publicly-funded primary care practices in England. Findings from the analysis of 145,000 reviews covering almost 7,700 primary care centers indicate that the quality of interactions with staff and bureaucratic exigencies are the key issues driving user satisfaction across England

Empirical Evaluation of Automated Sentiment Analysis as a Decision Aid

Research has consistently shown that online word-of-mouth (WOM) plays an important role in shaping customer attitudes and behaviors. Yet, despite their documented utility, explicit user scores, such as star ratings have limitations in certain contexts. Automatic sentiment analysis (SA), an analytics technique that assesses the “tone” of text, has been proposed as a way to deal with these shortcomings. While extant research on SA has focused on issues surrounding the design of algorithms and output accuracy, this research-in-progress examines the behavioral and interface design issues in regards to SA scores as perceived by their intended users. Specifically, in an online context, we experimentally investigate the role of product (product category) and review characteristics (review extremity) in influencing the perceived usefulness of SA scores. Further, we investigate whether variations in how the SA scores are presented to the user, and the nature of the scores themselves further affect user perceptions

Unfolding the influencing factors and dynamics of overall hotel scores

The hospitality and tourism industry was boosted by the help of hotel review sites, which consists in an increasing demand on the part of tourists. We extracted more than thirty thousand reviews from Tripadvisor to understand the variations in customers' perceptions of high/low end and chain/independent hotels and on which aspects this variation is most evident. We used sentiment analysis to assign a score to the aspects of each review. We compared machine learning algorithms, namely, random forest, decision tree and decision tree with adaBoost, to predict the overall score. Then, we used the Gini index to understand the aspects that most influence the overall score. Finally, we compared the reviews with temporal windows overtime with Jaccard index to characterize the dynamics of customer satisfaction focusing on three aspects: "Service", "Location" and "Sleep". Correlating the responses of the hotel to the users' reviews, we wanted to demonstrate the impact in the customers' perception of the hotel quality. The best performances were achieved by the decision trees which indicated that "Service" is the most influential aspect for satisfaction, while "Location" and "Sleep" were the aspects considered less important. By identifying the moments of drastic changes, we verified that "Service" is also the most related to the overall score. These analyses allow hotel management to track the trends of tourists' assessment in each category. Generally speaking, a focus on the "Service" should be done. However, an analysis, for a particular hotel, of the dynamics of the overall score to compare with its category would be advantageous.A indústria da hospitalidade e turismo foi impulsionada pela ajuda de sites de avaliações de hotéis, que leva a uma exigencia crescente por parte dos turistas. Extraímos mais de trinta mil avaliações do Tripadvisor para entender as variações nas percepções dos clientes de hotéis de alta/baixa gama e cadeia/independentes e quais os aspectos essa variação é mais evidente. Usámos sentiment analysis para atribuir uma pontuação aos aspectos de cada avaliação. Comparámos algoritmos de aprendizagem automática, nomeadamente, "random forest", "decision tree" e "decision tree with adaBoost", para prever a pontuação geral. Depois, usámos o índice de Gini para entender os aspectos que mais influenciam a pontuação geral. Por fim, comparámos avaliações com as janelas temporais ao longo do tempo com o índice de Jaccard para caracterizar a dinâmica de satisfação do cliente com foco em três aspectos: "Service", "Location" e "Sleep". Ao correlacionar as respostas do hotel com as avaliações, queriamos demonstrar o impacto na percepção dos clientes sobre a qualidade dos hoteis. Os melhores desempenhos foram alcançados pelo decision tree que indicou que "Service" é o aspecto mais influente para satisfação, enquanto que "Location" e "Sleep" foram os aspectos considerados menos importantes. Ao identificar os momentos de mudanças drásticas, constatámos que "Service" também é o mais relacionado à pontuação geral. Estas análises permitem que a gestão dos hoteis acompanhe as tendências da avaliação dos turistas em cada categoria. De um modo geral, um foco no serviço deve ser feito. No entanto, uma análise, para um hotel particular, da dinâmica da pontuação geral para comparar com sua categoria seria vantajosa

The Impact of Sentiment Analysis Output on Decision Outcomes: An Empirical Evaluation

User-generated online content serves as a source of product- and service-related information that reduces the uncertainty in consumer decision making, yet the abundance of such content makes it prohibitively costly to use all relevant information. Dealing with this (big data) problem requires a consumer to decide what subset of information to focus on. Peer-generated star ratings are excellent tools for one to decide what subset of information to focus on as they indicate a review’s “tone”. However, star ratings are not available for all user-generated content and not detailed enough in other cases. Sentiment analysis, a text-analytic technique that automatically detects the polarity of text, provides sentiment scores that are comparable to, and potentially more refined than, star ratings. Despite its popularity as an active topic in analytics research, sentiment analysis outcomes have not been evaluated through rigorous user studies. We fill that gap by investigating the impact of sentiment scores on purchase decisions through a controlled experiment using 100 participants. The results suggest that, consistent with the effort-accuracy trade off and effort-minimization concepts, sentiment scores on review documents improve the efficiency (speed) of purchase decisions without significantly affecting decision effectiveness (confidence)

Aspect-based sentiment analysis for social recommender systems.

Social recommender systems harness knowledge from social content, experiences and interactions to provide recommendations to users. The retrieval and ranking of products, using similarity knowledge, is central to the recommendation architecture. To enhance recommendation performance, having an effective representation of products is essential. Social content such as product reviews contain experiential knowledge in the form of user opinions centred on product aspects. Making sense of these for recommender systems requires the capability to reason with text. However, Natural Language Processing (NLP) toolkits trained on formal text documents encounter challenges when analysing product reviews, due to their informal nature. This calls for novel methods and algorithms to capitalise on textual content in product reviews together with other knowledge resources. In this thesis, methods to utilise user purchase preference knowledge - inferred from the viewed and purchased product behaviour - are proposed to overcome the challenges encountered in analysing textual content. This thesis introduces three major methods to improve the performance of social recommender systems. First, an effective aspect extraction method that combines strengths of both dependency relations and frequent noun analysis is proposed. Thereafter, this thesis presents how extracted aspects can be used to structure opinionated content enabling sentiment knowledge to enrich product representations. Second, a novel method to integrate aspect-level sentiment analysis and implicit knowledge extracted from users' product purchase preferences analysis is presented. The role of sentiment distribution and threshold analysis on the proposed integration method is also explored. Third, this thesis explores the utility of feature selection techniques to rank and select relevant aspects for product representation. For this purpose, this thesis presents how established dimensionality reduction approaches from text classification can be employed to select a subset of aspects for recommendation purposes. Finally, a comprehensive evaluation of all the proposed methods in this thesis is presented using a computational measure of 'better' and Mean Average Precision (MAP) with seven real-world datasets

Conceptualizing the Electronic Word-of-Mouth Process: What We Know and Need to Know About eWOM Creation, Exposure, and Evaluation

Electronic word of mouth (eWOM) is a prevalent consumer practice that has undeniable effects on the company bottom line, yet it remains an over-labeled and under-theorized concept. Thus, marketers could benefit from a practical, science-based roadmap to maximize its business value. Building on the consumer motivation–opportunity–ability framework, this study conceptualizes three distinct stages in the eWOM process: eWOM creation, eWOM exposure, and eWOM evaluation. For each stage, we adopt a dual lens—from the perspective of the consumer (who sends and receives eWOM) and that of the marketer (who amplifies and manages eWOM for business results)—to synthesize key research insights and propose a research agenda based on a multidisciplinary systematic review of 1050 academic publications on eWOM published between 1996 and 2019. We conclude with a discussion of the future of eWOM research and practice