Sentiment Analysis: Classifier and Summarizer

Abstract

Sentiment Analysis and summarization has a large number of application that are useful for determining the sentiment of the text and summarizing a big text into a small paragraph of few lines. Thus it has become an important topic to work on and for fulling the requirements of the customer. It has also become an important topic for researchers to focus on, as it is highly demanded and beneficial in different fields of product, services and growth of the business. At present when 89.9% of people are using social media platform, they express their reviews, feelings, emotions and share their comments and some exciting activities of their life through social media platform, so it becomes very important to analyses them and classify them as positive or negative, this can be done with the help of sentiment analysis. Also, to find the summary of a big document with large amount of data summarizer is very useful as we can get the summary of a document in the favorable number of line. The basic model of sentimental analysis classify the word as positive as negative with the help of some machine learning approaches, which will help in improving the quality of product and providing the service to the customer for building up a healthy competition in market and keeping the goodwill of the business . It also displays the output in the form of graph whose data is taken from social media platform. Sentimental analysis also helps in getting the summary of the document by picking the lines containing the words having maximum repetitions. It has been found that sentiment analysis able to classify the positive sentence by giving the output as 1 and negative sentences as 0. The model which is being built also graphically represents the classifications of positive and negative words picked from the dataset and it’s also useful in summarizing a document Thus sentiment analysis comes out to be very important for classify the unstructured data on social media platform and so there is always a scope of building a better model which is more accurate and efficient.