You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].

Este articulo no es un articulo de investigacion, ya que solo muestra como programar un robot mediante la herramienta de logica difusa. Este tema ya ha sido propuesto como solucion en navegacion de robots.
Solution: es
Why? This review is written in spanish. Hence, the label is 'es'.

New input: - Strengths: The approach described in the manuscript outperformed the previous approaches and achieved the state-of-the-art result. Regarding data, the method used the combination of market and text data. The approach used word embeddings to define the weight of each lexicon term by extending it to the similar terms in the document. - Weaknesses: Deep-learning based methods were known to be able to achieve relatively good performances without much feature engineering in sentimental analysis. More literature search is needed to compare with the related works would be better. The approach generally improved performance by feature-based methods without much novelty in model or proposal of new features. - General Discussion: The manuscript described an approach in sentimental analysis. The method used a relatively new method of using word embeddings to define the weight of each lexicon term. However, the novelty is not significant enough.
Solution:
en