Can you imagine manually sorting through thousands of tweets, customer support conversations, or surveys, or product and movie reviews? There’s just too much business data to process manually. Sentiment analysis helps businesses process huge amounts of data in an efficient and cost-effective way.
Sentiment analysis, also known as opinion mining, is essential as analysing customer feedback such as opinions in survey responses, social media conversations and product reviews allows business and brands to listen attentively to their customers and tailor products and services to meet their needs. Feature based sentiment analysis include feature extraction, sentiment prediction, sentiment classification and optional summarization modules. Feature extraction identifies those product aspects which are being commented by customers, sentiment prediction identifies the text containing sentiment or opinion by deciding sentiment polarity as positive, negative or neutral and finally summarization module aggregates the results obtained from previous two steps.
Gathering public opinion by analyzing big social data has attracted wide attention due to its interactive and real time nature. For this, recent studies have relied on both social media and sentiment analysis in order to accompany big events by tracking people’s behavior. Movie reviews are becoming more important with the evolution of the movie industry. Reviewers are posting reviews directly on movie pages in real time. With the vast amount of movie reviews, this creates an opportunity to see how the industry reacts to a specific product.
In this project we propose an adaptable sentiment analysis approach that analyzes IMDb movies’ review dataset. By using NLP, we will make the computer truly understand more than just the objective definition of the words. This analysis will help us segregate the data that has good as well as bad movie reviews.