text: This article aims at understanding the role played by the different words in a sentence, taking into account their order in the sentence. In sentiment analysis for instance, this capacity is critical to model properly negation. As state-of-the-art approaches rely on LSTM, the authors want to understand which information comes from which gate. After a short remainder regarding LSTM, the authors propose a framework to disambiguate interactions between gates. In order to obtain an analytic formulation of the decomposition, the authors propose to linearize activation functions in the network. In the experiment section, authors compare themselves to a standard logistic regression (based on a bag of words representation). They also check the unigram sentiment scores (without context). The main issue consists in modeling the dynamics inside a sentence (when a negation or a 'used to be' reverses the sentiment). The proposed approach works fine on selected samples. The related work section is entirely focused on deep learning while the experiment section is dedicated to sentiment analysis. This section should be rebalanced. Even if the authors claim that their approach is general, they also show that it fits well the sentiment analysis task in particular. On top of that, a lot of fine-grained sentiment analysis tools has been developed outside deep-learning: the authors should refer to those works. Finally, authors should provide some quantitative analysis on sentiment classification: a lot of standard benchmarks are widely use in the literature and we need to see how the proposed method performs with respect to the state-of-the-art. Given the chosen tasks, this work should be compared to the beermind system: http://deepx.ucsd.edu/#/home/beermind and the associated publication http://arxiv.org/pdf/1511.03683.pdf