# Sentiment Classification Using Convolutional Neural Networks
## 1 Introduction  
In the Big Data era, the amount of various data, such as image, video, sound, and text, is increasing exponentially. As text is the largest among them, studies related to text analysis have been actively conducted from the past to the present. In particular, text classification has drawn much attention because the text may have categorical labels such as sentiment (e.g., positive or negative), author gender, language category, or various types (e.g., spam or ham). For example, the users of Social Network Services (SNS) mostly represent their sentimental feeling, and they often share some opinions about daily news with the public or friends. Emotional analysis involves mood categories (e.g., happiness, joy, satisfaction, angry), while sentiment analysis involves categories such as positive, neutral, and negative. In this paper, we target the sentiment analysis that classifies the given text into one of sentiment categories. On websites about movies, people are likely to post their comments that probably contain sentiment or opinions. If such a sentiment is accurately predicted, then it will be applicable to various industrial fields (e.g., movie recommendation, personalized news-feed). Indeed, the international market of movies is growing much faster than before, so many companies (e.g., Netflix) provide movie recommendation services that essentially predict the sentiment or rating scores of customers.  
There have been many studies that have adopted machine learning techniques for text classification. Although the machine learning techniques have been widely used and have shown quite successful performance, they strongly depend on manually-defined features, where the feature definition requires much effort of domain experts. For this reason, deep learning techniques have been drawing attention recently, as they may reduce the effort for the feature definition and achieve relatively high performance (e.g., accuracy). In this paper, we aim at sentiment classification for text data and propose an architecture of the Convolutional Neural Network (CNN) [1; 2; 3], which is a type ofdeep learning model. We demonstrate the effectiveness of our proposed network through experimental comparison with other machine learning models.  
The contributions of this paper can be summarized as follows: (1) we design an architecture of two consecutive convolutional layers to improve performance for long and complex texts; (2) we provide a discussion about the architectural comparison between our models and two other state-of-the-art models; (3) we apply the CNN model to binary sentiment classification and achieved 80.96%, 81.4%, and 70.2% weighted F1 scores for Movie Review (MR) data [4; 5; 6], Customer Review (CR) data [7], and Stanford Sentiment Treebank (SST) data [8], respectively; and (4) we also show that our model achieved 68.31% for ternary sentiment classification with the MR data.  
The remainder of this paper is organized as follows. Section 2 reviews related studies about sentiment classification, machine learning, and deep learning models. Section 3 presents a detailed description of our proposed network. Section 4 describes the experimental settings and datasets and compares the proposed model with some other models. Thereafter, Section 5 discusses the experimental results. Finally, Section 6 concludes this paper.