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Discovering Influence of Yelp Reviews Using Hawkes Point Processes

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 296)


With the development of technology, social media and online forums are becoming popular platforms for people to share opinions and information. A major question is how much influence these have on other users’ behavior. In this paper, we focused on Yelp, an online platform for customers to share information of their visiting experiences on restaurants, to explore the possible relationships between past reviews and future reviews of a restaurant through multiple aspects such as star-ratings, user features and sentiment features. By using the lasso regression model with review features processed through Hawkes Process Model and B-Spline basis functions as the modeling of restaurant basic performance, average star-ratings, low star-ratings and sentiment features of past reviews have been found to have significant influence on future reviews. Due to the limited dataset, we performed simulation on restaurants’ reviews using Multinomial Logistic Regression and re-built the model. A verification process has been performed eventually using Logistical Regression. The simulation and the verification results have been found to support the prior findings which indicate that influence between past and future reviews does exist, and can be revealed on multiple aspects.


  • Yelp reviews
  • Hawkes Point Process
  • Data mining
  • Sentiment features

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  • DOI: 10.1007/978-3-030-82199-9_7
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Correspondence to Yichen Jiang .

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8 Appendix

8 Appendix

1.1 8.1 B-Spline Basis Function

The basic framework of B-Spline curve has been created by Schoenberg on 1946 [17], and has been developed to adjust different application such as modeling of 3-D geometry shape or interpolation of fluctuating data points for smoothing purpose. A k-order B-Spline curve is composed by a set of linear-combined control points \(P_i\) and B-Spline basis functions denoted as \(N_{i,k}(t)\), and each control point is associated with a basis function in a recurrence relation such that:

$$\begin{aligned} N_{i,k}(t) = N_{i,k-1}(t)\frac{t-t_i}{t_{i+k-1}-t_i}+N_{i+1,k-1}(t)\frac{t_{i+k}-t}{t_{i+k}-t_{i+1}} \end{aligned}$$
$$N_{i,1} = {\left\{ \begin{array}{ll} 1&{} if \quad t_i \le t \le t_{i+1}\\ 0&{} otherwise \end{array}\right. }$$

The shape of B-Spline basis function is determined by the knot vector:

$$\begin{aligned} T = (t_0, t_1,...,t_{k-1},t_k,t_{k+1},...,t_{n-1}, t_n, t_{n+1},..., t_{n+k}) \end{aligned}$$

The number of elements of the knot vector is defined by the sum of number of control points and the order of the B-Spline curve (n+k+1).

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Jiang, Y., Porter, M. (2022). Discovering Influence of Yelp Reviews Using Hawkes Point Processes. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 296. Springer, Cham.

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