# A Domain-Independent Classification Model for Sentiment Analysis Using Neural Models
## 1 Introduction  
Sentiment analysis is the task of recognizing positive and negative opinions of users regarding different purposes, e.g., users' opinions about movies, products, music albums, and many other fields. To provide a better definition, the sentiment is referred to as a judgement, opinion, attitude, or emotional state prompted by feeling. Sentiment analysis is an automated process in which, by using the natural language processing (NLP), the subjective information is computationally identified, analyzed, and classified into positive, negative, or neutral to specify the sentiment of that text which is the result of its author's attitude [1]. There are different types of sentiment analysis, the most popular types are classified and described in the following:  
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3. Aspect-based Sentiment Analysis: The results in this type are achieved after splitting the text into different aspects and then assign each aspect a corresponding sentiment. For instance, the result of aspect-based sentiment analysis on a special product's review \"It is so easy to use but Insanely Expensive\" would be (a) Ease of use: positive and (b) Price: negative due to the nature of this type, it is mostly utilized in customer-centric businesses to have a deeper understanding of customer's requirements [4].  
The sentiment analysis field has many applications. For example, in businesses and organizations, they need to find consumer or public opinions regarding their products and services. Individual consumers may also need to know the evaluation of other users of a product before purchasing it. Moreover, they might be interested in others' opinions concerning political candidates before making a voting decision in a political election. Furthermore, nowadays data are published enormously and freely on the Web, but with no data quality assurance, it is left to the readers to decide whether they believe it or not. This results in high demand for advanced fact checking techniques and applications that contribute to the assurance of data quality. In particular, users surfing the Web are more often inflicted with harm/damage by inconsistent information. In addition, designing and developing such fact checking systems need robust models of sentiment analysis. This task for fact checking detection can be fulfilled when fact checking systems are provided by a general or universal model that can be trained once and then applied to other reviews. Surely, this model should show high performance to increase the accuracy of such fact checking systems.  
[PERSON] in [5] discussed the importance and effects of the challenges in sentiment analysis is the domain-dependence. Moreover, the author concluded that the nature of the topic and the review structure determine the suitable challenges for the evaluation of sentiment reviews. Hence, building a generalized model is a challenge that should be considered by researchers in this research field.  
This work focuses on providing a generalized model for sentiment analysis. It has two main contributions: (a) it shows that convolutional neural networks (CNN) combined with our review to vector algorithm can lead to design models that can be trained once and work well using other types of data that might even be related to a different domain and (b) it shows high performance using different precision metrics compared to other approaches from the state of the art that use same datasets for evaluation. We believe that this is one of the few works that address the generalization capabilities of deep models w.r.t. domain-Independence.  
The remainder of this paper is organized as follows: Section 2 outlines a set of related works. In Section 3, we present the proposed approach. Section 4 lists datasets that are used for evaluation purposes. In Section 5, we cover experimental results. Section 6 illustrates a detailed discussion containing the major contribution of the paper. Section 7 concludes the paper and gives an overview of future work.