Sentimental Analysis In English

Abstract—In sentimental analysis, the emotional polarity of a given text is analysed, and classified as positive, negative or neutral. A more difficult problem is to done the classification into dif- ferent moods such as happy,sad,anger,fear,love etc. Analysing a natural language for emotion extraction is not at all an easy task for a computer. This paper focuses on identifying the ap- propriate emotional class in Malayalam text at sentence level.
Multi-class emotion extraction has been successfully implemented for English and other European languages. For the south Indian language Malayalam, no significant work has yet been done on emotion extraction.The only existing work used a semantic orientation approach for emotion detection.This paper

A training set is used by an automatic classifier to learn the differentiating characteristics of documents, and a test set is used to validate the performance of the automatic classifier. A number of machine learning techniques have been adopted to classify the reviews. Machine learning techniques like Naive Bayes (NB), maximum entropy(ME) and support vector machines (SVM) have achieved great success in text categorization. In paper [] the extraction of different classes of emotions from sentences was done by using supervised machine learning technique, Multinomial Naive Bayes (MNB). Here a bag of word approach is used to capture the emotions.The unigrams are mainly used for this and the bigrams and trigrams are used to capture lower order dependencies. The experiments with different feature sets selected using Weighted log-likelihood score (WLLS) shows that the MNB classifier provides good results across all emotion classes.The paper [] discussed about classifiers for sentiment analysis of user opinion towards political candidates through comments and tweets sing Support
Vector Machine(SVM). The goal is to develop a classifier that performs sentiment analysis, by labeling the users comment to positive or negative. From which classify the text

The corpus can be domain-dependent or general depending on the task at hand.Being a measure of the degree of statistical dependence between two words, the purpose of PMI is to determine how closely two words are related.
Since an emotion concept can often be expressed through various words(e.g.’glad’ or ’joy’ for ’happiness’),propose to use a few words rather than just one generic word representing the entire emotion category.The idea is that if a word belongs to an emotion category, it will be closely related to most of the representative words that comprise the emotion concept instead of a random off-chance association with a single word.The
PMI scores between w i and each representative word of an emotion category are used to compute the PMI score of w i and the category.Let K j be a set of r representative words for emotion concept e j .The semantic relatedness score between an affect word w i and an emotion category e j is calculated as shown below:
P M I(w i , e j ) = ( r g=1