Definition: You are given a paper review either in Spanish or English language. Based on the given review, evaluate the paper by classifying it to one of the five classes: (1) very negative, (2) negative, (3) neutral, (4) positive, and (5) very positive. Note that URLs in the text have been replaced with [Link].

Positive Example 1 -
Input: El artículo presenta un interesante desarrollo de un caso, como una propuesta más de metodología de inicio para la didáctica de la lógica de programación. Las conclusiones presentadas no tienen mucho fundamento en términos del logro del objetivo de inducir de esta manera el razonamiento lógico.
Output: very positive

Positive Example 2 -
Input: Paper fácil de leer y bien escrito.  Está en la línea de los trabajos de INFONOR.  Sin embargo, no queda claro cómo aplica la 'Teoría de la relevancia', es demasiado escueto en este ámbito del artículo que a mi parecer es lo más importante. Por lo que no es posible dar una opinión certera sobre el trabajo.  Se sugiere expandir la sección 2.2, en lo referente a cómo los aspectos para determinar la relevancia son utilizados o mezclados para determinar si una 'news' es relevante o no.  La herramienta elaborada es interesante como también los elementos utilizados en su desarrollo.
Output: neutral

Negative Example 1 -
Input: Unless there is a strong info sci and business IT community for the conference, and unless the paper addresses its shortcomings (which are significant) - it just doesn't measure up.
Output: positive

Negative Example 2 -
Input: El artículo presenta un interesante desarrollo de un caso, como una propuesta más de metodología de inicio para la didáctica de la lógica de programación. Las conclusiones presentadas no tienen mucho fundamento en términos del logro del objetivo de inducir de esta manera el razonamiento lógico.
Output: very negative

Now complete the following example -
Input: The paper introduces a segmentation procedure for breast tissue images using SVM.  General comments:  a) Concerning the proposed approach: The segmentation method introduced only considers local color/gray value information. Perhaps introducing other features related to the membrane's local (e.g. the membrane has symmetry properties that can be exploited) or global (e.g. continuity, approximately circular shape) properties may improve the segmentation results.  Feature selection is also a key component to improve performance. Use a feature selection procedure.  b) Concerning the experimentation: You should describe the experiments with sufficient detail so others can replicate them: a) Make your data publicly available b) Explain how you have selected the classifier's parameters (parameter selection in an SVM is critical and may improve notably the performance).  You should join Figs 10 and 9 in a single plot so we can better see the differences.  You only provide qualitative segmentation results (Figs. 12, 14, 15), but quantitative results are also very important. Specially when it comes to comparisons. You should quantitatively compare your algorithm with other competing works in the literature.  c) Concerning paper organization: The paper is well written and clearly introduces the problem, describes the relevant literature and presents the solution. I have a few comments concerning the paper organization: a) Problem statement and literature review (intro and theoretical framework sections) are in my opinion a bit long, compared to other important sections, e.g. feature extraction and experimentation which are just one paragraph each. b) Although English is in general correct, there are some small mistakes that could be solved by having it read by a professional. c) There are two "proposed method" sections  Well written paper. Good literature review.  It only shows very preliminary results.
Output: 


Answer: very positive