Text classification algorithms exploration on sentiment analysis

Abstract

Internet provides us with an abundance of useful tools and data. However, it also generates a vast quantity of data that may bewilder us. There must be a technique for automatically processing these data. Here, text classification becomes useful. Text classification is the algorithm-based process of categorizing data inputs into distinct labels. For instance, email software utilizes it to assess if an email should be filtered into the spam folder, social media forums use it to classify postings into labels that are relevant to the topic, etc. Text categorization is utilized in a variety of businesses, including search engines, sentiment analysis, emergency response systems, chatbots, etc. Review websites have emerged in recent years where customers may share their opinions on a business or a product. The review is extremely emotive but crucial to the company. It is possible to accurately assess the reviews for the sentiment they present through text classification. This paper compares the efficacy of various text classification algorithms for sentiment analysis.