The sentimental analysis is an automated process that enables an individual to understand an opinion regarding a subject either in written or spoken language. Research has shown that approximately 2.5 quintillion bytes of data are generated on a daily basis (Liu, 2012). As a result, sentiment analysis has developed into a fundamental tool in interpreting data. Companies, therefore, have leveraged their positions to get vital insights and further automate different sets of processes. Thus, the sentimental analysis is used for prediction and analysis. With almost 80% of data existing in the form of unstructured audios, texts, and videos, sentimental analysis has found applicability in a vast array of areas (Liu, 2012).
Some of the common areas that this process applies include in social media monitoring to decipher the feelings and attitudes of individuals. It has also found valuable use in the voice of the customer (VOC) services as a means of tracking customer reviews, assessing survey responses, and analyzing the competitors. It also has direct use in the field of business especially in analytics or in other additional situations where text analysis has importance. One of the reasons why sentimental analysis is preferred is due to its efficiency where data processing occurs in seconds. Many businesses today are continuing with the incorporation of sentimental analysis into their systems.
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The steps in sentimental analysis begin by gathering data. After collection, the next step is the text cleaning that involves the removal of punctuations and stop-words. The next step is known as the sentimental generation where the users find meaning attached to a statement. Stock independent variable follows, and lastly, the process ends with prediction. The two methods for polarity identification include the lexicon-based approach and the machine learning approach. The lexicon-based approach makes assumptions that the text in question has emotionally connoted words that indicate the attitude of the writer. On the contrary, the machine learning approach involves a polarity determined by the use of classifiers such as the Naïve Bayes. Both mechanisms have yielded good results with an accuracy of about 80% (Pajupuu, Altrov, & Pajupuu, 2016).
References
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167.
Pajupuu, H., Altrov, R., & Pajupuu, J. (2016). Identifying polarity in different text types. Folklore. Electronic Journal of Folklore, (64), 126-138.
