5,807 research outputs found

    Analysis of Yelp Reviews

    Full text link
    In the era of Big Data and Social Computing, the role of customer reviews and ratings can be instrumental in predicting the success and sustainability of businesses. In this paper, we show that, despite the apparent subjectivity of user ratings, there are also external, or objective factors which help to determine the outcome of a business's reviews. The current model for social business review sites, such as Yelp, allows data (reviews, ratings) to be compiled concurrently, which introduces a bias to participants (Yelp Users). Our work examines Yelp Reviews for businesses in and around college towns. We demonstrate that an Observer Effect causes data to behave cyclically: rising and falling as momentum (quantified in user ratings) shifts for businesses.Comment: 24 pages, 20 figures and 5 table

    Crowd control : organizing the crowd at Yelp

    Get PDF
    This dissertation investigates how businesses are able to align the collective actions of a disconnected crowd with the strategic goals of the organization. I examined this questions within the context of the business review website Yelp through a quantitative analysis of nearly 60,000 business reviews, 17 in-depth qualitative interviews with reviewers, and a two-year ethnography. Interpreting the results of this data within the framework of the collective action space (Bimber, Flanagin, & Stohl, 2012) indicates that Yelp is able to manage the contributions of a relatively small subset of reviewers through the Yelp Elite Squad. Rather than simply motivating more reviews, the Elite Squad encouraged reviewers to interact more personally with other reviewers and accept increased institutional engagement with Yelp. In encouraging members of the crowd to produce online reviews within this context, Yelp was able to use organizational culture as a control strategy for encouraging Elite reviewers to adopt a pre-mediated reviewing approach to their reviews. This increased the frequency of moderate reviews and decreased the frequency of extreme reviews. This behavior ultimately furthers the organizational goals of Yelp, as moderate reviews are considered to be more helpful for reviews of businesses. Finally, implications for crowdsourcing, big data analysis, and theory are discussed

    Using social media to assess the consumer nutrition environment: comparing Yelp reviews with a direct observation audit instrument for grocery stores

    Get PDF
    Objective To examine the feasibility of using social media to assess the consumer nutrition environment by comparing sentiment expressed in Yelp reviews with information obtained from a direct observation audit instrument for grocery stores. Design Trained raters used the Nutrition Environment Measures Survey in Stores (NEMS-S) in 100 grocery stores from July 2015 to March 2016. Yelp reviews were available for sixty-nine of these stores and were retrieved in February 2017 using the Yelp Application Program Interface. A sentiment analysis was conducted to quantify the perceptions of the consumer nutrition environment in the review text. Pearson correlation coefficients (ρ) were used to compare NEMS-S scores with Yelp review text on food availability, quality, price and shopping experience. Setting Detroit, Michigan, USA. Participants None. Results Yelp reviews contained more comments about food availability and the overall shopping experience than food price and food quality. Negative sentiment about food prices in Yelp review text and the number of dollar signs on Yelp were positively correlated with observed food prices in stores (ρ=0·413 and 0·462, respectively). Stores with greater food availability were rated as more expensive on Yelp. Other aspects of the food store environment (e.g. overall quality and shopping experience) were captured only in Yelp. Conclusions While Yelp cannot replace in-person audits for collecting detailed information on the availability, quality and cost of specific food items, Yelp holds promise as a cost-effective means to gather information on the overall cost, quality and experience of food stores, which may be relevant for nutrition outcomes

    Content Analysis of Hospital Reviews From Differing Sources: Does Review Source Matter?

    Get PDF
    Social media has had an impact on how patients find and evaluate medical professionals and their experiences of modern healthcare. Qualitative research in healthcare has increased its focus on social media. The present study examined 497 reviews of hospitals in the Pittsburgh area across three websites: Google, Yelp, and Healthgrades. Using computerized content analysis tools (CATA), we analyzed positive and negative comments to identify key themes. Key themes and words included “doctor,” “hospital,” “staff,” and “time.” These findings highlight the importance of medical staff to patient experience. Results indicated that Yelp had the lowest average rating. CATA also revealed that the central term for Google reviews was “hospital,” for Healthgrades reviews it was “doctor,” and the central term for Yelp reviews was “patient.” These central terms reflect the focus of each website. The present study highlights the importance of healthcare professionals understanding the source of reviews and being cautious about how social media comments are used in decision-making about the practice. Future research should try to expand this approach to other cities and countries to evaluate cross-cultural effects on social media comments

    Learning representations from heterogeneous network for sentiment classification of product reviews

    Get PDF
    There have been increasing interests in natural language processing to explore effective methods in learning better representations of text for sentiment classification in product reviews. However, most existing methods do not consider subtle interplays among words appeared in review text, authors of reviews and products the reviews are associated with. In this paper, we make use of a heterogeneous network to model the shared polarity in product reviews and learn representations of users, products they commented on and words they used simultaneously. The basic idea is to first construct a heterogeneous network which links users, products, words appeared in product reviews, as well as the polarities of the words. Based on the constructed network, representations of nodes are learned using a network embedding method, which are subsequently incorporated into a convolutional neural network for sentiment analysis. Evaluations on the product reviews, including IMDB, Yelp 2013 and Yelp 2014 datasets, show that the proposed approach achieves the state-of-the-art performance

    Subtopics in Yelp Reviews

    Get PDF
    Yelp is a review platform that connects people to local businesses. It is a very popular platform that helps customers decide which business to choose. It relies on crowd sourced plain text reviews. From the business’s description some facts can be determined, such as category and location. However, more detailed description can be extracted from the reviews. Discovering latent topics and subtopics in Yelp reviews, can help summarize the reviews to gain knowledge. For example, we can deduce that reviews related to the Restaurant category tend to emphasize on service, food, order etc. Additionally, one can deduce positive or negative feedback on each topic and subtopic. In this project, we study the problem of content topic discovery using probabilistic and other models in a Yelp dataset. Various experiments were performed to extract word features, by trying to keep the initial context and sentence structure with the use techniques such as Document to bag of words, Word Embedding, Parts of Speech (POS) tagging and Term Frequency-Inverse Document Frequency (TFIDF). In our approach, we discover topics in the Yelp corpus with the use of Machine Learning techniques. Specifically, we use the Latent Dirichlet Allocation (LDA), the Latent Semantic Analysis (LSA) and the K-Means technique. These unsupervised learning techniques divide the corpus into latent topics that summarize the review text and highlights the insight of it. The methods are compared using the Coherence Model and the resultant LDA model is visualized using pyLDAvis. Finally, by comparing our techniques, we conclude that the K-Means using Word Embeddings on particular Parts of Speech tagged words gives best results, but is time consuming. On the contrary, LDA applied on cleaned corpus containing POS tagged words with TF-IDF is much faster albeit topics report loss of context in comparison to K-Means
    corecore