# Sentiment Classification Using Convolutional Neural Networks
## 2 Background  
### Machine Learning for Sentiment Classification  
The sentiment can be defined as a view of or an attitude toward a situation or event. It often involves several types of self-indulgent feelings: happiness, tenderness, sadness, or nostalgia. One may define the sentiment labels as a polarity or valence (e.g., positive, neutral, and negative) or several types of emotional feeling (e.g., angry, happy, sad, proud). The definition of sentiment labels will affect the outcome of the sentiment analysis, so we need to define the sentiment labels carefully. There have been studies that defined three or more sentiment labels (e.g., opinion rating scores, emotional feelings) [9; 10; 11; 12; 13], and some studies adopted two-dimensional labels (e.g., positive and negative) [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. Although there has been much performance improvement in the field of sentiment analysis, the binary classification of sentiment still remains challenging; for example, the performance (e.g., accuracy) of recent studies varied from 70-90% in terms of the characteristics of data. In this paper, we aim at the binary classification (positive or negative) and the ternary classification (positive, neutral, and negative). There have been many studies on classifying sentiments using machine learning models, such as Support Vector Machine (SVM), Naive Bayes (NB), Maximum Entropy (ME), Stochastic Gradient Descent (SGD), and ensemble. The most widely-used features for such machine learning models have been n-grams. [PERSON] [14] used unigram features for sentiment binary classification and obtained 88.94% accuracy using the SVM. [PERSON] and [PERSON] [15] adopted unigram and bigram features for sentiment binary classification and achieved 84.4% accuracy for movie review data [27] using the SVM. [PERSON] [20] tackled binary classification with unigram and bigram features and achieved 86.1% accuracy for translated Amazon product review data. In [21], [PERSON] utilized a combination of unigram, bigram, and trigram features and obtained 72.78% accuracy using the SVM. [PERSON] and [PERSON] [28] aimed at five-class (e.g., strong positive, positive, neutral, negative, and strong negative) sentiment classification and obtained 81% accuracy using the SVM with bigram features. In the study of [PERSON] and [PERSON] [18], they utilized the SVM along with the semantic analysis model for the sentiment binary classification of Twitter texts and achieved 89.9% accuracy using unigram features. [PERSON] et al. [19] also used the SVM with n-gram features and obtained 88.94% accuracy for sentiment binary classification. [PERSON] et al. [25] adopted unigram features for sentiment binary classification and achieved 79% using the NB for translated Urdu tweets data. All of these studies that utilized n-gram features generally achieved 70-90% accuracies, and the most effective model was the SVM. There also have been studies that have defined hand-crafted features for sentiment classification. [PERSON] and [PERSON] [29] used affective lexicon, misspelling, and emoticons as features and obtained87% accuracy for ternary classification using the SVM. [PERSON] [22] defined three scores (e.g., positivity, negativity, and objectivity) as features and achieved 67% precision and 66% recall for binary classification using the Logistic Regression (LR) classifier. [PERSON] et al. [16] tackled binary classification using the SVM and achieved 67.8% accuracy for Twitter texts, where they adopted two kinds of target-independent features (e.g., twitter content features and sentiment lexicon features). [PERSON] and [PERSON] [23] used the number of positive words and the number of negative words as features and achieved 86.7% accuracy for binary classification with Twitter texts. [PERSON] and [PERSON] [17] combined unigram features and their own Twitter-specific features and obtained 90% accuracy for binary classification using the SVM.