Literature Review Chapter: Sentiment

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[. . .] Lange wrote that "studies show that consumers are much more trusting of other user's opinions than of the marketing produced by the manufacturer" (Lange, 2010, ) and the vast majority of text that those customers see, according to Lange is unstructured text that has keywords that evoke positive images. Promoting those positive images would be a good thing for company's who wish to enhance their bottom line(s). According to Lange, from the company perspective listening and analyzing what people are saying, reading or thinking about your products and services is an important step in engaging with your audience via dialogue (Lange, 2010).

Another study said that analyzing the text plays a crucial role in many of the important decisions that a company has to make on a regular basis. The study determined that "information hidden or stored in unstructured data can play a critical role in making decisions, understanding and conducting other business functions" (Prasad & Ramakrishna, 2010, p. 2201).

According to the current literature, it would seem that many of the uses for analytic techniques would have to do with profitability, improving products, or (at the very least) enhancing the marketing that companies undertake in order to promote their products or services. Text analysis can also take the form of providing historical context and enlightenment into historical documents, including what some of the great philosophers meant when they wrote the words they wrote. Accordingly, text mining is used as the technique to determine those details, facts and figures.

Prasad and Ramakrishna state that data mining from textual sources allows for knowledge discoveries from text(s), but that what makes text analytics different is that it looks to determine or provide knowledge of a structure that is inherent in the text itself. To them 'text analytics is the answer to overcome unstructured data' (Prasad & Ramakrishna, 2010). Again according to Prasad et al. The textual sources should help to overcome that lack of structure, but it should also allow for a statistical analysis of the extracted data to determine concepts and patterns that can then be categorized or classified. Text analysis does not always mean the written word itself, it can also be applied to video, audio, images, websites, and (of course) documents. An example of categorization might be when a media such as websites are data mined, the researcher is often looking to analyze not only the text but the identifying terms, numerical expressions and even concepts. Text analysis can also include text wrapping and annotations that are analyzed as freeform, however that type of analysis is normally corresponsive with domain-specific entities be identified as text wrapping (Surehka & Sreekumar, 2005).

With the different methods for analyzing texts and the varied arenas in which it is employed, recalling that text analytics started out in the educational field grounds the theory in a manner that it might not otherwise be grounded. Theory is fine, but having the theory battered by the educational process certainly lends a more certain air to the entire process. As one expert succinctly stated "a carefully chosen, word, phrase, or sentence could unlock the door to a constellation of meanings" (Welsh, 2010, p. 28).

Another expert in the educational field Nel Noddings described the process of how an analytic philosopher might, "for example, analyze the concept of teaching or of education…and considerable attention would be given to the various linguistic contexts in which the concept appears" (Noddings, 2007, p. 43). Text analysis in the education environment makes just as much sense as using text analytics in business or government.

Both business and education benefit from the textual analytical process. School and business administrators can make decisions with the organized data gleaned from text analytics. One author wrote that the implementation of text analytics if it is executed in the right manner assists executives and administrators by offering an effective approach to make current and ongoing disposition decisions confidently (Santangelo, 2009).

With the advent of the internet and other technological advances using analytics makes sense regarding all kinds of text, mainly because such technology allows for the solving of a variety of problems regarding the administration of data gleaned through that technology. Santangelo provided his take concerning data management when he wrote "it is stated that in solving the problem of large, unmanaged data repositories that increase an organization's cost and legal exposure, the need for the development of text analytics search technology along with the information management strategy is critical" (Santangelo, p. 23).

As the current literature shows, text analytics can be quite helpful in a variety of ways and in diverse areas of study such as business, technology and education. Further breakdown of text analytics can occur with phrase, sentence and word analytics, which are discussed further in this paper.

Phrase Analytics

Phrase analytics is a method for analyzing phrases in and out of context in order to determine what the author meant (or maens) with the use of certain phrases. One expert determined that "in the twelfth century, Dominicus Gundissalinus famously used intellectus as an equivalent for Ar. 'aqi, while Gerard of Cremona seems to have preferred ratio (Turnhout, 2006, p. 697). The question could be asked as to why anyone would wonder about this phrase usage from the medieval times; the answer could provide the basis for phrase analytics. Phrase analytics is used in a variety of situations and it is especially helpful in information retrieval (IR) systems using natural language processing (NLP).

Because the system is able to determine in nanoseconds what type of information is requested by analyzing keywords it makes a lot of sense to use analytics to help the system work at top efficiency. Without phrase analytics (complemented by word analytics of course) the system would quickly become overburdened and inefficient. Use of modern technology to access information "requires that the NLP used must be extraordinarily efficient in both its time and space requirements" (Evans, Chengxiang, 1996, p. 18).

With the trillions of bits of information now currently being retrieved on a daily basis through the use of key words and phrases it would make little sense to not employ phrase analytics in this specific instance. Without phrase analysis the system would be running at a speed of one or two sentences per second which would not make much sense at all. Evans and Chengxiang showed results from experiments involving indexing and extracted subcompounds and found that phrase analytics "improves both recall and precision in an information retrieval system" and they also discovered that phrase analysis techniques showed promise regarding book indexing and automatic thesaurus extraction (Evans & Chengxiand, p. 17).

Current literature also provides evidence of other uses of phrase analytics. One of those other uses includes the historical aspect of literature. Many experts and layman throughout history have been fascinated by what previous authors have meant with their writings, musings and treatise. Gaining insights into history through the words of leaders, philosophers, and writers (to name a few) can lead to understanding and comprehending societal traditions, mores and means. Such an understanding can also assist modern society in making choices that guide the community and the individual. Knowing that "Gerard never rendered that phrase "quod est quia…instead, he routinely used the words "et illud ideo quoniam" a more akward reading it might seem, but also arguably a more literal version of the Arabic, which does not include anything corresponding to the verb 'to be" (Galen, 1490).

The question could remain of whether such exactness in word or phrase could mean anything important, to which the historian would likely respond with a highly enthusiastic yes.

Knowing the experiences of those that came before us; what those who came before today's world experienced, thought, dreamed or worried about would provide invaluable data for today's world as well. As McVaugh wrote concerning medieval studies, "these identifications, however preliminary they may be at the moment, may wll already be of some value" (McVaugh, 2009, p. 112).

Phrase analytics also allows a view into the emotions of these peoples from the past. How did they feel about what was taking place in their world, during their times? Diid they despair for what the future held? Were they happy with their lives? These (and more) are all questions that can be answered to no small degree by using phrase analysis. A number of experts and studies have verified this approach asking the question of how emotions are to be investigated philosophically by providing evidence that at least among philosophers of emotion of the past decades you will find mainly two answers. On the one hand, there are those who are committed to some form of conceptual analysis (e.g. Kenny 1963; Lyons 1980; Nussbaum 2001; Roberts 2003; Solomon 1976) with "conceptual analysis" meaning investigations into the meanings of words by reflecting on… [END OF PREVIEW]

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Sentiment.  (2011, January 29).  Retrieved September 16, 2019, from

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"Sentiment."  29 January 2011.  Web.  16 September 2019. <>.

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"Sentiment."  January 29, 2011.  Accessed September 16, 2019.