Information technology, computer science, etc. have been developed more and more in many countries in the world. Their subfields have already had many very crucial contributions to everyone life: production, politics, advertisement, etc. Especially, big data semantics, scientific and knowledge discovery, and intelligence are the subareas that are gaining more interest. Therefore, the authors display semantics for massive data sets fully in this chapter. This is very significant for commercial applications, studies, researchers, etc. in the world.
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The massive corporations, large-scale organizations, and etc. have already been built more and more from the strong development of the economies of the countries in the word for the recent years. They have already generated a lot of information, knowledge, and etc. We have already called these information, knowledge, and etc. as many big data sets (BIGDSs).
According to our knowledge, the BIGDS has been a data set which has been millions of data samples (or billions of data records) or a massive size (or a big volume) about over billions of bytes, etc.
These large-scale data sets (LARSDSs) have been wondered whether they have been very crucial for the corporations, organizations, and etc. Therefore, the corporations, organizations, and etc. have needed whether the massive data sets (MASDSs) have been handled fully, successfully, and etc. In addition, they have needed whether the BIGDSs have been extracted many significant values automatically, etc.
The LARSDSs has comprised many different areas such as natural language processing (NLP), machine learning (ML), data mining (DM), artificial intelligence (AI), expert systems (ES), and etc. There have been many commercial applications and many surveys of these fields which have been studied, developed, and etc. for the MASDSs. Furthermore, these areas have been very significant for the corporations, organizations, and etc. in the world. However, there have not been enough the commercial applications and the studies for the BIGDSs yet in the world for the recent years.
Based on the above proofs and our opinion, we have presented the semantic analysis (SEMANA) for the LARSDSs in this book chapter.
According to our opinion, the classification has been a process of identifying the semantic values and the sentiment polarities of many words, many phrases, many sentences, many documents, and etc.
The sentiment polarity has been positive, negative, or neutral. When a word has been displayed positive attitudes such as like, love, etc., this word has been the positive polarity. When a word has been shown negative attitudes such as dislike, hate, etc., this word has been the negative polarity. When a word has been presented neutral attitudes such as drink, eat, etc., this word has been the neutral polarity.
Based on the opinion polarity of a word, when a phrase has been shown positive attitudes, this phrase has been the positive polarity. When a phrase has been presented negative attitudes, this phrase has been the negative polarity. When a phrase has been displayed neutral attitudes, this phrase has been the neutral polarity.
According to the sentiment polarity of a word and the opinion polarity of a phrase, when a sentence has been shown positive attitudes, this sentence has been the positive polarity. When a sentence has been displayed negative attitudes, this sentence has been the negative polarity. When a sentence has been presented neutral attitudes, this sentence has been the neutral polarity.
Based on the semantic polarity of a word/phrase/sentence, when a document has been presented positive attitudes, this document has been the positive polarity. When a document has been shown negative attitudes, this document has been the negative polarity. When a document has been displayed neutral attitudes, this document has been the neutral polarity.
The semantic value (or the sentiment score, or the valence) has been a value of a word/phrase/sentence/document. The valence of a word has been greater than 0 when the sentiment polarity of this word has been the positive. The sentiment score of a word has been less than 0 when the opinion polarity of this word has been the negative. The opinion value of a word has been as equal as 0 when the semantic polarity of this word has been the neutral.
According to the valence of a word, the sentiment score of a phrase has been greater than 0 when the opinion polarity of this phrase has been the positive. The opinion value of a phrase has been less than 0 when the semantic polarity of this phrase has been the negative. The valence of a phrase has been as equal as 0 when the valence of this phrase has been the neutral.
Based on the opinion value of a word, and a phrase, the valence of a sentence has been greater than 0 when this sentence has been the positive polarity. The opinion score of a sentence has been less than 0 when this sentence has been the negative polarity. The sentiment value of a sentence has been as equal as 0 when this sentence has been the neutral polarity.