Text-Based Sentiment Analysis with Classification Techniques—A State-of-Art Study

Social media acts as a bridge between people to widely share any data and communication. In recent years, the textual data content is increasing rapidly, where the text can contain any kind of information about people, product or service. Manually reading each text from online is not possible, and also, it is a challenging task to decide whether the user has positive stance or negative stance on the topic. To solve this problem, text processing techniques and algorithms are required. Sentiment analysis is the technology that processes any online text and classifies it into positive, negative and neutral. To analyze online content, new models are proposed by incorporating the machine learning concept. The unstructured information from online documents is analyzed and classified as results, which has been described as user sentiment analysis. The outcome of sentiment analysis can be used for business development, understand the customer expectations and also to know the public opinion toward a particular product or service. This paper focuses on various sentiment analysis processes and also the most used classification techniques from machine learning concepts.