Applying Network Science to Semantic Text Analysis

Every human language typically has many meanings apart from the obvious meanings of words. Moreover, a word, phrase, or entire sentence may have different connotations and tones. It explains why it’s so difficult for machines to understand the meaning of a text sample. Therefore, in semantic analysis with machine learning, computers use Word Sense Disambiguation to determine which meaning is correct in the given context.

• In semantic hashing documents are mapped to memory addresses by means of a neural network in such a way that semantically similar documents are located at nearby addresses.
• A next step in refining our research would be to find ways to split the largest communities into smaller communities that reflected sentiment more effectively.
• It’s an essential sub-task of Natural Language Processing and the driving force behind machine learning tools like chatbots, search engines, and text analysis.
• Speaking about business analytics, organizations employ various methodologies to accomplish this objective.
• Looking for the answer to this question, we conducted this systematic mapping based on 1693 studies, accepted among the 3984 studies identified in five digital libraries.
• However, there is a lack of studies that integrate the different research branches and summarize the developed works.

In semantic analysis with machine learning, computers use word sense disambiguation to determine which meaning is correct in the given context. By knowing the structure of sentences, we can start trying to understand the meaning of sentences. We start off with the meaning of words being vectors but we can also do this with whole phrases and sentences, where the meaning is also represented as vectors.

Need of Meaning Representations

However, specially in the natural language processing field, annotated corpora is often required to train models in order to resolve a certain task for each specific language . Besides, linguistic resources as semantic networks or lexical databases, which are language-specific, can be used to enrich textual data. Thus, the low number of annotated data or linguistic resources can be a bottleneck when working with another language. There are important initiatives to the development of researches for other languages, as an example, we have the ACM Transactions on Asian and Low-Resource Language Information Processing , an ACM journal specific for that subject. Wimalasuriya and Dou , Bharathi and Venkatesan , and Reshadat and Feizi-Derakhshi consider the use of external knowledge sources (e.g., ontology or thesaurus) in the text mining process, each one dealing with a specific task.

This text also introduced an ontology, and “semantic annotations” link text fragments to the ontology, which we found to be common in semantic text analysis. We also discovered that the largest communities had many one or two word reviews which were not very related to each other, like the examples above of “wow” and “ok ok”. We theorized that these types of one word judgements weren’t long enough to be properly assessed in terms of trigrams, so were not necessarily linked to others with similar sentiments. A next step in refining our research would be to find ways to split the largest communities into smaller communities that reflected sentiment more effectively.

Content Extraction

Other sparse initiatives can also be found in other computer science areas, as cloud-based environments , image pattern recognition , biometric authentication , recommender systems , and opinion mining . Natural language processing semantic text analysis is an area of computer science and artificial intelligence concerned with the interaction between computers and humans in natural language. The ultimate goal of NLP is to help computers understand language as well as we do.

Speaking in terms of k-grams, we outputted the number of k-grams that differed between the strings. The hamming algorithm was a challenging implementation, since at this point we had not written code to vectorize our data set, which meant the function was written before we had test cases. In real application of the text mining process, the participation of domain experts can be crucial to its success. However, the participation of users is seldom explored in scientific papers.

Semantic classifiers

For example, this article suggested that text analysis is moving away from a bag of n-gram linear vector methods, since network science models allow for accurate analysis without n-grams. Our cutoff method allowed us to translate our kernel matrix into an adjacency matrix, and translate that into a semantic network. Text semantics is closely related to ontologies and other similar types of knowledge representation. We also know that health care and life sciences is traditionally concerned about standardization of their concepts and concepts relationships. Thus, as we already expected, health care and life sciences was the most cited application domain among the literature accepted studies. This application domain is followed by the Web domain, what can be explained by the constant growth, in both quantity and coverage, of Web content.

Thus, this paper reports a systematic mapping study to overview the development of semantics-concerned studies and fill a literature review gap in this broad research field through a well-defined review process. Semantics can be related to a vast number of subjects, and most of them are studied in the natural language processing field. As examples of semantics-related subjects, we can mention representation of meaning, semantic parsing and interpretation, word sense disambiguation, and coreference resolution. Nevertheless, the focus of this paper is not on semantics but on semantics-concerned text mining studies. This paper aims to point some directions to the reader who is interested in semantics-concerned text mining researches.

Recommenders and Search Tools

Finding HowNet as one of the most used external knowledge source it is not surprising, since Chinese is one of the most cited languages in the studies selected in this mapping (see the “Languages” section). As well as WordNet, HowNet is usually used for feature expansion [83–85] and computing semantic similarity [86–88]. Specifically for the task of irony detection, Wallace presents both philosophical formalisms and machine learning approaches. The author argues that a model of the speaker is necessary to improve current machine learning methods and enable their application in a general problem, independently of domain. He discusses the gaps of current methods and proposes a pragmatic context model for irony detection.

The difficulty inherent to the evaluation of a method based on user’s interaction is a probable reason for the lack of studies considering this approach. Despite the fact that the user would have an important role in a real application of text mining methods, there is not much investment on user’s interaction in text mining research studies. A probable reason is the difficulty inherent to an evaluation based on the user’s needs. In empirical research, researchers use to execute several experiments in order to evaluate proposed methods and algorithms, which would require the involvement of several users, therefore making the evaluation not feasible in practical ways.

How does semantic analysis work?

If this knowledge meets the process objectives, it can be put available to the users, starting the final step of the process, the knowledge usage. Otherwise, another cycle must be performed, making changes in the data preparation activities and/or in pattern extraction parameters. If any changes in the stated objectives or selected text collection must be made, the text mining process should be restarted at the problem identification step.