There are as many sentiment analysis techniques as there are reasons for conducting sentiment analysis. Analysis techniques are employed to discern sentence, phrase, word and text meanings, and predictive, machine-related, emotional and psychological aspects are measured by sentiment analysis as well. This literature review will attempt to navigate the various avenues presented by such diverse usage of sentiment analysis and provide information that categorizes and differentiates between the various techniques employed.
The review will focus on a number of different analytical techniques, including sentiment analysis, predictive analysis, text, phrase, sentence and word analytics, and why and how the different arenas approach the use of their specific style of analysis.
Sentiment analysis has been described as using a lexicon "with information about which words and phrases are positive and which are negative" (Wilson, Wiebe, Hoffman, 2009, p. 400) and as an analytical tool that "focuses on identifying positive and negative opinions, emotions and evaluations expressed in natural language" (Wilson, et al., 2009. P. 399). Keeping that description in mind, sentiment analysis can take place using a variety of methods to evaluate diverse areas of interest, including business, medical and education fields. Most analysis takes place using a step-by-step process no matter in which area of study the evaluation is taking place.
In the Wilson study the next step was to tag the lexicon clues into one of three polarities; positive, negative or neutral. Wilson categorized the words in order to determine at what level positive, negative or both were used in the various texts, phrases and sentences.
A word in the lexicon is deemed positive if (out of context) the word evokes a positive feeling, deemed negative if it evokes negativity, and is tagged as both if it is both positive and negative, and neutral if it deems no positivity or negativity at all. However, as Wiebe states in a 1994 report, sentiment analysis is a type of subjectivity analysis (Wiebe, 1994).
A certain amount of subjectivity is going to be found in almost every study, but it would seem that analyzing sentiment would be especially difficult in a country where the ways that the English language is spoken is as diverse as the people who speak it. One of the problems of sentiment analysis concerns the manner in which people speak, as well as verbal and non-verbal communications. One expert states "Humans communicate with each other in ways such as languages, facial expressions or gestures that possibly cover their sentiments" (Yang, Lin, Chen, 2008, p. 331). Sentiment analysis is subjective then because of the words used, but also the context in which they are used, as well as how the participant uses them. The question could be asked, if it is so subjective, then why do it? The answer might come from Yang et al. study that discovered "the analysis of sentiments helps us identify the latent meanings associated with communication and thus enhances opinion analysis, as well as mining individuals sentiments towards specific subjects or events" (Yang et al., p. 332).
If that answer rings true, then sentiment analysis can work well for a variety of purposes. Some of those purposes include such things as gauging customer (or potential customer) responses to products or services, Current literature provides a number of examples of such uses, including 2009 study by Nan et al.
Their study determined that "sentiment analysis helps understand and predict the behavior of the customer through analysis of the texts in blogs, forums and survey responses" (Nan, Xun, Xinli, Chao, Desheng, 2009, p. 227) and more specifically the study found that sentiment analysis is employed to probe into the correlations between information sentiment and asset price volatility (.p228). In other words, some researchers even use sentiment analysis to make money on the stock market. Since the purchase or sale of stocks and bonds is often based on emotion rather than business acumen, it actually makes quite good sense to employ sentiment analysis in such a manner.
Most experts, however, use sentiment analysis for much more banal endeavors. A number of textual studies called sentiment analysis the "text mining technique employed by a number of researchers to determine the attitude of a speaker or a writer with respect to a specific topic" (Ahmad & Almas 2005; Chaovalit & Zhou, 2005; Turney 2001, Turney 2002, Turney and Littman, 2003). Using sentiment analysis in this manner is what is considered the 'normal' approach of sentiment analysis, although it is true that studies are used every day to determine whether business customers are satisfied with services and products specifically regarding the language they use when talking to customer service representatives, and perhaps more importantly the language the customers hear from those same reps (Kho, 2010, p.11).
One other interesting avenue taken by a recent study implementing sentiment analytics was a study that looked at two prominent characterizations of morality; what is good and what is right is in direct correlation with happiness.
The claim was made by the study that it is good and right to do what our moral sentiments tell us to do, and the claim that it is good and right to do what would maximize the amount of happiness in the universe (Fehige, Frank, 2010, p. 141). Here was a study that sought to determine morality with the use of sentiment analysis. What the study determined was that there was one common way of drawing distinctions of a moral sentiment as a motivational force, or in other words; "a person feels good or bad about something in a certain kind of way, and this comprises or causes a tendency to bring about or continue that thing in the positive case, or to prevent it or stop it in the negative case" (p. 141).
Sentiment analytics are also used in predictive analysis. According to one expert some of the questions that predictive analytics attempts to answer include "which of my customers are likely to churn? What kinds of offers will persuade my customers to stay or new customers to buy? Price? Service options? Which potential customers are likely to be highly profitable? Which are likely to cost me money? What should I show this surfer when I serve the next page? (Monash, 2005, p. 36). Specifically, businesses are looking to analyze what word, phrase, sentence or text is going to be successful in drawing in customers, keeping them buying the goods or service, what pitch will work best with which client or customer and the language that will be most profitable for company use.
Siddharth Taparia, a senior director for software developer SAP, recently stated to Industry Week that "predictive analytics is becoming an advanced business intelligrence" (Alpern, 2010, p. 48).
In fact, Best Buy recently incorporated predictive analytics regarding the behavior of its customers regarding purchasing patterns and why and what customers bought as compared to what was advertised. According to Alpern, Best Buy's first step was to "study customers buying patterns to discern predictive patterns of purchasing (p. 48).
As shown by the literature, sentiment analysis can be used in a variety of areas, from purchasing, to morality to deciding exactly what an author or writer meant by the use of a certain phrase, text, sentence, or word.
When referring to sentiment analysis, many experts are referring to analyzing the meaning of words in certain texts, sentences, phrases or contexts. There are a number of levels where sentiment analysis regularly takes place. One of the most common areas is in text (referring to passages, articles, books, journals and even text messages). One of the most comprehensive descriptions of text analytics comes from Grimes when he states that text analytics applies "statistical and/or linguistic and/or structural techniques to identify, tag, and extract entities, concepts, relationships, and events (features) within document sets" ( Grimes, 2008, p. 11). Furthermore, he defines text analytics even further by describing the process as a way to apply statistical pattern-matching and similarity techniques in order to classify certain documents or text along organized features according to specific categorized taxonomy" (Grimes, 2008). In fact, Grimes was also part of a study in 2009 that provided further classification and description by providing a process for using text analytics. He defined text analytics as the use of software to simply the process of annotating the information and then analyzing those annotations and other extracted information from a textual source or sources. The reason behind this process was to extract from the test, "entities, concepts, topics, facts and attitudes" (Grimes, 2009).
Some of the reasons behind the analyzing of text is not only to determine what the author meant upon writing the particular text, but other insight can be gained as well. The author is not the only person of interest, the person affected by the text is also affected. The question could be asked as to why such interest would be shown regarding what individuals think upon reading certain text, but examining the question from a business person's viewpoint; if a company could…