# Sentiment Analysis Based Requirement Evolution Prediction
## 1 Introduction  
The product feature requirements are the descriptions of the features and functionalities of the target product. This provides a way for a product developer to get and understand the expectations of users of the product. Product feature systems are undergoing continuing changes and a continuous updating process to satisfy users' requirements. Such requirements' evolution plays an important role in the lifetime of a product system in that they define possible changes to product feature requirements, which are one of the main issues that affect development activities, as well as product features. However, the requirements of real product are often hidden and implicit. Product developers need to gather varying requirements from the users to know their ideas on what the product should provide and which features they want the product to include. Without the ability to discover these changing feature requirements from the users, they fail to make decisions about what features the product should provide to meet users' requirements. More importantly, in a real application, we need to identify the feature requirements of given products and the opinions being expressed towards each feature at different granularities from users. Taking smartphones as an example, some users care about general features such as appearance and screen size, while others may pay more attention to more specific features such as the user interface and running speed of the processor. On the one hand, different requirement information is interested in different granularities of features of a product. Analysis of specific product features and sentiments at a single granularity cannot capture the requirements of all users. We need to capture such different granularity requirements from user reviews. Therefore, it is necessary to convey the specific features and sentiments at different granularities of products to product developers so that they can easily understand the different levels of user concerns. On theother hand, the sentiment polarities of many words depend on the specific feature. For example, the sentiment \"fast\" is positive for the feature \"processor\", but it would be negative for describing the feature \"battery\" of a smartphone. Therefore, it is more challenging to model feature granularity and to incorporate sentiment. Sentiment analysis is the computational study of people's opinions and attitudes towards entities such as products and their attributes. Understanding and explicitly modeling hierarchical product features and their sentiment are an effective way to capture different users' requirements. In this paper, we demonstrate the hypothesis for the task of hierarchical product features requirement evolution prediction by sentiment analysis with a recurrent neural network and the Hierarchical Latent Dirichlet Allocation (HLDA). It presents a hierarchical structure of features and sentiments to product developers or requirement engineers so that they can easily navigate the different requirement granularities. From the viewpoint of product developers, it is easy to capture the requirement information of different users on different granularities of product features. They can understand what users think of each feature of the product application by taking both the features and sentiment into consideration. The approach is explored to discover and predict the evolution of different granularity feature requirements of the product from the users' review text. The approach combines deep Long Short-term Memory (LSTM) with the HLDA to predict product feature requirements of different hierarchies. The different hierarchical product features with their sentiment could be presented to a product developer as a detailed overview of the users' primary concerns with the information about which product features are positive and which features are negative, which is used to suggest changes in product requirements from users. It can be used to decide whether or not the feature is a candidate for a product requirement change.