Bilinear Elastic Net or **BEN** is a bilinear version of Elastic Net that has been proposed for performing text regression (see reference at the bottom). Bilinearity was introduced in order to jointly model words and users in an approach for predicting voting intentions. From this page, you may download an implementation of BEN for MATLAB. Note that this is a prototype beta version; for more up-to-date versions, please get in touch.

Download BEN v0.7 beta (December, 2013)

**Prerequisites**

>> **SPAMS toolbox** (for MATLAB) must be installed

>> **v2struct** function should be present in your path

**Usage hints**

Suppose that **Var1** is a set of **N** n-grams, **Var2** is a set of **M** users and that we consider **T** time instances. Then:

>> **Var1**'s vector space representation (**H**) is a **TxN** matrix, where **H _{ij}** is the frequency of n-gram

**j**during time instance

**i**

>>

**Var2**is an

**(MxT)xN**matrix (

**U**), where

**U**is the frequency of n-gram

_{ij}**j**for user

**ceil(i,T)**during time instance

**mod(i,T)**

BEN is not limited to the scenario above; **Var1** and **Var2** can represent various other things. The input of function **ben** is a **struct** with a multitude of fields. All fields are explained inside the m-file of the function; you may also view them on the command window by typing "**help ben**". Following the notation above, **H** and **U** are represented by the struct fields **var1_vsr** and **var2_vsr** respectively. Two useful spots in the SPAMS documentation for understanding the parameter settings for **mexFistaFlat** function (the one we use to implement Elastic Net) are: spot_A & spot_B.

**Functions or scripts included in the package**

ben: The main function implementing BEN

formVar1Matrix: Updates the vector space representation of **Var1** based on the weights of **Var2**

centerX: Function for centering

meanSerror: Computes the mean squared error (MSE)

demo: Mock example of usage

**Reference**

V. Lampos, D. Preoţiuc-Pietro and T. Cohn (2013). A user-centric model of voting intention from Social Media. ACL '13, pp. 993-1003.