Sentiment analysis for movie reviews using embedding words with semantic orientation

Sentiment analysis is an opinion mining that analyzes people's emotions and attitudes on a given topic. The field of sentiment analysis is conducted by Natural Language Processing. In this research, a new sentimental method has been proposed, based on semantic analysis of the reviews, incorporated into the word Embedded as a deep learning framework. Also, in this research proposing a semantic classifier based on part of speech technique that used to identify the polarity of reviews as "positive" or "negative". The concept of Deep learning was implemented in the feature extraction step as an embedding word technique, that used to improve the accuracy of classifying. Due to the limitations in recent conventional bag-of-word models and their ability to capture word meaning similarities, that produce highly dimensioned vectors to throw in learn contextual information about words. The dataset of Internet Movie Database movie reviews was concentrated. Multiple tests were carried out, testing and evaluating the effectiveness of the proposed method using dataset. The recorded results showed a better accuracy by the embedding words as a deep learning technique, compared with the other vector representations.