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Background: With the tremendous increase in the use of social networking sites for sharing the emotions, views, preferences etc. a huge volume of data and text is available on the internet, there comes the need for understanding the text and analysing the data to determine the exact intent behind the same for a greater good. This process of understanding the text and data involves loads of analytical methods, several phases and multiple techniques. Efficient use of these techniques is important for an effective and relevant understanding of the text/data. This analysis can in turn be very helpful in ecommerce for targeting audience, social media monitoring for anticipating the foul elements from society and take proactive actions to avoid unethical and illegal activities, business analytics, market positioning etc. Method: The goal is to understand the basic steps involved in analysing the text data which can be helpful in determining sentiments behind them. This review provides detailed description of steps involved in sentiment analysis with the recent research done. Patents related to sentiment analysis and classification are reviewed to throw some light in the work done related to the field. Results: Sentiment analysis determines the polarity behind the text data/review. This analysis helps in increasing the business revenue, e-health, or determining the behaviour of a person. Conclusion: This study helps in understanding the basic steps involved in natural language understanding. At each step there are multiple techniques that can be applied on data. Different classifiers provide variable accuracy depending upon the data set and classification technique used.

This chapter provides a basic understanding of processes and models needed to investigate the data posted by users on social networking sites like Facebook, Twitter, Instagram, etc. Often the databases of social networking sites are large and can't be handled using traditional methodology for analysis. Moreover, the data is posted in such a random manner that can't be used directly for the analysis purpose; therefore, a considerable preprocessing is needed to use that data and generate important results that can help in decision making for various areas like sentiment analysis, customer feedback, customer reviews for brand and product, prevention management, risk management, etc. Therefore, this chapter is discussing various aspects of text and its structure, various machine learning algorithms and their types, why machine learning is better for text analysis, the process of text analysis with the help of examples, issues associated with text analysis, and major application areas of text analysis.