Text Sentiment Analysis Using Artificial Intelligence Techniques

Abstract

In today’s world, data is being generated with such high velocity and variety that analyzing such large volumes of data to extract meaningful results is a taxing job and manually impossible. Developing methods to analyze such large volumes of data for the purpose of finding hidden patterns to achieve meaningful interpretations is necessary for many organizations for making informed decisions. Sentiment analysis is one such method that is used to interpret the emotions represented by text data. There exists a broad range of applications for sentiment analysis. Public opinion is an important “business insight”. All businesses are interested in analyzing the consumer behavior, understanding their needs, understanding their likes and dislikes and their buying patterns, and as more and more people are becoming vocal about their preferences, the data required by these companies is becoming readily available on blogs and social media platforms, but the analysis of such large amount of raw data to derive useful conclusions is a hectic task if tried to perform manually. In such cases, sentiment analysis can be used to analyze this raw data. Sentiment analysis can be used for a variety of needs ranging from understanding the public opinion of a government policy to assigning movie ratings from analysis of viewer ratings. This method is especially an important factor in social media monitoring to gain a wider public opinion about a topic. NLP tools available today can be used to efficiently analyze this raw text and classify texts as positive, negative, or neutral. Different machine learning algorithms like random forest, logistic regression, and support vector machine can be used to train the model using the features extracted by the NLP techniques. The trained model can then be used to predict the polarity of the raw input data. ROC and PR curves have been plotted to check the accuracy of the algorithms.