To represent the proposed approach to classify sentiment, suppose that the collection consists of emotion, i.e.. Given the limited set of examples for each emotion, we try to represent the collection as a hierarchy of dominant meanings.

In this definition, each emotion is represented by a finite set of examples. The question now is how can we use those examples to construct dominant meanings of the corresponding emotion? In other words, those examples include some words that almost come with the corresponding emotion. The challenge is how to determine those words.

Each example is represented by a fixed set of words.

The’s represent the frequency of word occurs in example which belongs to emotion. This frequency is computed as the number of times that the occurs in the.

Our goal is to choose the top- words, which can represent the dominant meanings of emotion. To do that, we proceed as follows. Suppose that word symbolizes emotion.

・ Calculate the values of

・ Suppose that is the frequency of emotion, which appears in example, where

・ Calculate the maximum value of,

・ Calculate the maximum value of,

where

・ Choose, which satisfies

・ Finally, consider the dominant meaning probability

・ . (4)

Therefore, we divide by the maximum value of the frequency of, and then we normalize the results by dividing by the number of examples in collection. Based on formula (3), we clearly have.

The proposed system creates sevens models one for each emotion: joy, fear, anger, sadness, disgust, shame, and guilt.

For each emotion, we have a collection of N examples

. For each collection, we apply the formula from (1) to (4).

After applying formulas, we get a set of dominant meanings each word in the set has value for a word and in emotion.

We rank the terms of collection in decreasing order according to formula (4). As a result, the dominant meanings of the emotion can be represented by the set of words that is corresponds to the set; i.e..

Therefore, we select the top-N values of to form motion dominant meaning tree (EDMT). EDMT represents seven emotions suggested by (Klaus, 1994) as a tree. Each emotion is joined with a slave word. This slave is represent a dominant meaning and associated with the dominant meaning probability of that emotion as shown in Figure 2. In this paper we put the top-N of values as an arbitrary value.

Accordingly, we can create seven models to represent the emotion. Each model is a set called emotion dominant meaning models. Each contains the top-N dominant meaning probability:

The corresponding word set of is represented as:.

For a new example, represents a word in the new example. For each emotion, we compute the model value as flowing:

where

, for each

The emotion detection algorithm returns the emotion that represents a set of words. The algorithm uses Equation (6) to compute the model value for each emotion for the example. Therefore, it calculates the highest value and then returns the index of this value. This index is used to determine the emotion.

The dataset uses ISEAR dataset [9] that contains emotional statements. ISEAR contains 7666 sentences (as shown in Table 1). The dataset is collected from 1096 participants with different cultural background who completed questionnaires about seven emotions: anger, disgust, fear, sadness, shame, joy, and guilt.

Most of text classification methods use keyword-based methods with thesaurus. In contrast, we use the dominant meaning methods as features to improve accuracy and refine the categories. To build the dominant meaning tree, we use 60% of ISEAR dataset for seven emotion categories (as shown in Table 2): anger,

disgust, fear, sadness, shame, joy, and guilt.

Stop words were removed in all examples for examples: for, an, the, a, an, another, but, or, yet, so, towards, before, etc.

Based on the Equation (1) to (5), we can build the dominant meaning tree of seven emotion categories, as shown in Figure 2.

Each node contains one emotion. Each emotion is associated with top-N dominant meaning words based. The node between word and the emotion is labeled with its dominant meaning probability as shown in Figure 2. To determine N value, we have to conduct some experimentations with different N values to figure out which N reflects a considerable results. The following subsection presents the accuracy of the proposed method to classify emotion examples.

The goal of the experiments is to measure the accuracy of the proposed algorithm to predict a single emotional label given an input sentence. We follow Cohen’s Kappa [10] to measure the accuracies of the experiment. We use average precision, recall, and F-measure to measure the classification accuracy.

In this experiment, we use ISEAR dataset to figure out the performance of our proposed mechanism. We used a Java programing language to create a class file to implement Emotion Detection Algorithm. This program classified the tested data in one emotion. The results of precision and recall are shown in Figure 3.

The precision and recall of our proposed approach shows a considerable performance comparing to those in related works.

In his classification he found that using SVM produced better results for sadness (F1 = 0.733) which is better than our approach for sadness (F1 = 0.67). In contrast, our approach has better results in others classes such as anger (F1 = 0.66), disgust (F1 = 0.47), fear (F1 = 0.56), shame (F1 = 0.55), joy (F1 = 0.58), and guilt (F1 = 0.50). Where Balahur results were for anger (F1 = 0.38), disgust

(F1 = 0.264), fear (F1 = 0.49), shame (F1 = 0.43), joy (F1 = 0.46), and guilt (F1 = 0.42).

Danisman and Alpkocak [11] used the ISEAR collection and used vector space models (VSM) to categorize 801 examples. Our approach showed a significant results anger (F1 = 0.38), joy (F1 = 0.46), and sadness (F1 = 0.67) compared to Danisman and Alpkocak (2008) for anger (F1 = 0.242), joy (F1 = 0.496) and sadness (F1 = 0.371).

On the other hand, in order to test the performance of our proposed approach with alternative methods for emotion detection, we chose the work done by Balahur et al. [12] , as shown in Table 3.

The results of 10-fold cross validation using Support vector machine to classify the whole set of 1081 examples initially chosen. We found that using dominant meaning classifier produced better results all categories than using the method of SVM in Balahur et al. [12] , as shown in Table 3, where our proposed method produced the most accurate results for Sadness class with Precision (27.2) and Recall (60.2). Using 10-fold cross validation with SVM (Balahur, 2011) produced also the most accurate results for sadness class with Precision (0.707) and Recall (0.77). However, our proposed method produces a lower value for precision for two classes “Anger” with (20.2) and “Shame” with (20.2), Balahur’s results produced a lower value for precision a class “Disgust” with 0.292.

Figure 4 shows F1 measure for the results of Dominant meaning approach and 10-fold cross validation using SVM [12] . As we see both our proposed approach and Balahur’s approach have a similar function for drawing F1 measure. We see that the top value for the graph for both approach recorded for “Sadness” class and the bottom value for the graph for both approach recorded for “Disgust” class.