Abstract
The article describes the use of software, written by the author, to analyze some Italian literature works from the last century by using sentiment analysis. The software is lexicon-based, with a sentiment Italian dictionary including about 30,000 positive and negative words. To set the sentiment of each word, a scale of points ranging from 0 to 100 was introduced. To check the overall sentiment in literary works, the software makes use of a new parameter, the index of positivity. It is found that sentiment analysis is an efficient way to detect the tendency of opinions (positive or negative) in literature works. The results of this analysis coincide with the critics on the classification of authors’ tendencies. The index of positivity could be useful also for bookstores: the customers, checking the index, would expect in advance, which is the overall tendency in a literary work. In this case, it would be easier to select a certain kind of work, according to the taste or the wish of customers.
Highlights
In the last two decades, sentiment analysis has become very popular in computational linguistics; Sentiment analysis studies, among other things, the people’s opinions, sentiments and emotions (Liu, 2012)
To analyze the overall tendency of a text, Psychoword uses the arithmetic mean of all the level of positivity (LP), which is equal to an index of positivity (IP)
It was found that sentiment analysis is reliable using a lexicon-based dictionary
Summary
In the last two decades, sentiment analysis has become very popular in computational linguistics; Sentiment analysis studies, among other things, the people’s opinions, sentiments and emotions (Liu, 2012). The results help to understand the sentiment tendency (positive, negative, or neutral) or to classify opinions and emotions in a text without the need to read it. This kind of analysis is popular among companies of e-commerce to understand the taste of customers as fast as possible. The analysis results do not always show the truth: for example, often sentiment analysis is not able to detect the irony in a text Such limitation can be a problem when analysing the text is a literature work. Large dictionaries with classic tendencies (positive, negative or neutral) can quite be enlarged and included in some software to perform the analysis
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