After the selection phase, 1693 studies were accepted for the information extraction phase. In this phase, information about each study was extracted mainly based on the abstracts, although some information was extracted from the full text. The results of the accepted paper mapping are presented in the next section. In Natural Language, the meaning of a word may vary as per its usage in sentences and the context of the text. Word Sense Disambiguation involves interpreting the meaning of a word based upon the context of its occurrence in a text.

LSI is also an application of correspondence analysis, a multivariate statistical technique developed by Jean-Paul Benzécri in the early 1970s, to a contingency table built from word counts in documents. Another model, termed Word Association Spaces is also used in memory studies by collecting free association data from a series of experiments and which includes measures of word relatedness for over 72,000 distinct word pairs. Given a query of terms, translate it into the low-dimensional space, and find matching documents . Find similar documents across languages, after analyzing a base set of translated documents (cross-language information retrieval).

Sentiment Analysis

We interact with each other by using speech, text, or other means of communication. If we want computers to understand our natural language, we need to apply natural language processing. It is the first part of semantic analysis, in which we study the meaning of individual words. It involves words, sub-words, affixes (sub-units), compound words, and phrases also. However, machines first need to be trained to make sense of human language and understand the context in which words are used; otherwise, they might misinterpret the word “joke” as positive.

Foxworthy found a ”cutoff” value through taking the eigenvector of the kernel matrix, and created his network by marking an edge in an adjacency matrix for each pair of texts whose hamming similarity value was above the cutoff. The adjacency matrix corresponded to a semantic network from which Foxworthy extracted communities and sentiment keywords to characterize the communities. LSA assumes that words that are close in meaning will occur in similar pieces of text . Values close to 1 represent very similar documents while values close to 0 represent very dissimilar documents.

Mathematical Model of an Ontological-Semantic Analyzer Using Basic Ontological-Semantic Patterns

This paper takes the ontology of products available in Mobile Commerce as an example and tries to find out the importance of Heuristic search for ontology and how it is helpful for predictive analysis and recommendation system. LSI automatically adapts to new and changing terminology, and has been shown to be very tolerant of noise (i.e., misspelled words, typographical errors, unreadable characters, etc.). This is especially important for applications using text derived from Optical Character Recognition and speech-to-text conversion.

These researchers applied an importance index to a citation network generated through the Web of Science to create a keyword framework of taxonomy in scientific fields. The shortest path lengths of the network were the determining factor in the network analysis, since the researchers used shortest path lengths between keywords to find strongly connected components within the network. Therefore, the shortest path statistics determined the clustering and eventual categorization of the text. The researchers found that their network accurately expressed scientific taxonomies, and that border communities in the network revealed interested subcategories of the data.

Text Extraction

It is not our objective to present a detailed survey of every specific topic, method, or text mining task. This systematic mapping is a starting point, and surveys with a narrower focus should be conducted for reviewing the literature of specific subjects, according to one’s interests. Bos presents an extensive survey of computational semantics, a research area focused on computationally understanding human language in written or spoken form. He discusses how to represent semantics in order to capture the meaning of human language, how to construct these representations from natural language expressions, and how to draw inferences from the semantic representations.

• This lexical resource is cited by 29.9% of the studies that uses information beyond the text data.
• Network-based representations, such as bipartite networks and co-occurrence networks, can represent relationships between terms or between documents, which is not possible through the vector space model [147, 156–158].
• Implement a Connected Inventory of enterprise data assets, based on a knowledge graph, to get business insights about the current status and trends, risk and opportunities, based on a holistic interrelated view of all enterprise assets.
• This is where a semantic text analysis engine like 3RDi Search comes to the rescue of the enterprises and their data analysis challenges.
• To store them all would require a huge database containing many words that actually have the same meaning.
• This paper reports a systematic mapping study conducted to get a general overview of how text semantics is being treated in text mining studies.

The most popular example is the WordNet , an electronic lexical database developed at the Princeton University. Depending on its usage, WordNet can also be seen as a thesaurus or a dictionary . Figure 5 presents the domains where text semantics is most present in text mining applications.

Word Sense Disambiguation:

Clustering is a way to group documents based on their conceptual similarity to each other without using example documents to establish the conceptual basis for each cluster. This is very useful when dealing with an unknown collection of unstructured text. So a search may retrieve irrelevant documents containing the desired words in the wrong meaning. For example, a botanist and a computer scientist looking for the word “tree” probably desire different sets of documents.

The mapping reported in this paper was conducted with the general goal of providing an overview of the researches developed by the text mining community and that are concerned about text semantics. This mapping is based on 1693 studies selected as described in the previous section. We can note that text semantics has been addressed more frequently in the last years, when a higher number of text mining semantic text analysis studies showed some interest in text semantics. The lower number of studies in the year 2016 can be assigned to the fact that the last searches were conducted in February 2016. Natural language processing is a way of manipulating the speech or text produced by humans through artificial intelligence. Thanks to NLP, the interaction between us and computers is much easier and more enjoyable.

Sentiment Analysis with Machine Learning

The original term-document matrix is presumed too large for the computing resources; in this case, the approximated low rank matrix is interpreted as an approximation (a “least and necessary evil”). This matrix is also common to standard semantic models, though it is not necessarily explicitly expressed as a matrix, since the mathematical properties of matrices are not always used. All mentions of people, things, etc. and the relationships between them that have been recognized and enriched with machine-readable data are then indexed and stored in a semantic graph database for further reference and use. The relationships between the extracted concepts are identified and further interlinked with related external or internal domain knowledge.

The distribution of text mining tasks identified in this literature mapping is presented in Fig. Classification corresponds to the task of finding a model from examples with known classes in order to predict the classes of new examples. On the other hand, clustering is the task of grouping examples based on their similarities. Classification was identified in 27.4% and clustering in 17.0% of the studies. As these are basic text mining tasks, they are often the basis of other more specific text mining tasks, such as sentiment analysis and automatic ontology building.

• Thus, semantic analysis helps an organization extrude such information that is impossible to reach through other analytical approaches.
• The first step of the analytical approach is analyzing the meaning of a word on an individual basis.
• Algorithms split sentences and identify concepts such as people, things, places, events, numbers, etc.
• The second phase of the process involves a broader scope of action, studying the meaning of a combination of words.
• Besides, we can find some studies that do not use any linguistic resource and thus are language independent, as in [57–61].
• Semantic Analysis is a subfield of Natural Language Processing that attempts to understand the meaning of Natural Language.