Deep Learning Models for Sentiment Analysis

Saba Tahir

Abstract: Deep Learning Models Based Sentiment Analysis Application of multiple layers of artificial neural networks for the learning tasks is called deep learning. In the research field, deep learning is a powerful machine learning technique. It has the ability to learn multiple levels of representations and abstractions from data, which can solve both supervised and unsupervised learning tasks. Deep learning uses multiple layers of non-linear processing units for feature extraction and classification. Sentiment analysis is one of the active research areas in Natural Language Processing. There exist numerous techniques to perform sentiment analysis task, which include both supervised and unsupervised methods. Types of supervised machine learning method include Support Vector Machines (SVM), Maximum Entropy, Na?ve Bayes, etc. Types of unsupervised machine learning methods include sentiment lexicons, grammatical analysis, and syntactic patterns. Application of deep learning to sentiment analysis has been very popular now a days. The reason to choose deep learning models, as it provides improved performance and accurate results over learning tasks. Deep neural network methods will perform both feature extraction and classification for document and short text. The application of different deep learning models on sentiment analysis. Sentiment Analysis Sentiment analysis is a technique that comes under the field of natural language processing. The process of identifying human emotions and thinking is termed as sentiment analysis, which is also known as opinion mining. It classifies whether the given text is positive or negative or sometimes neutral also, based on the classification level on a given document or sentence. There exist several approaches to accomplish the sentiment analysis task. This task is achieved by identifying the sentiment or opinion of the subjective element within a text. The approaches that are used to classify a piece of text are according to the opinions expressed in it, i.e. either positive or negative or neutral. The analyzing piece of text can be sentence or document or anything. The sentiment analysis is accomplished by classifying the methods into machine learning and lexicon-based approach. Again the machine learning approach is classified into supervised and unsupervised machine learning techniques. Under supervised learning there exists mainly Support Vector Machine (SVM), Neural Networks (NN), Na?ve Bayes (NB), Maximum Entropy (ME) approaches.

Keywords: deep learning, sentiment analysis, Artificial intelligence