A Survey of Semantic Analysis Approaches SpringerLink

In order to fill this gap, we undertake a comprehensive discussion of semantic text classification vs. traditional text classification. Furthermore, this survey highlights the advantages of semantic text classification algorithms over the traditional text classification algorithms. Semantics is a branch of linguistics, which aims to investigate the meaning of language. Semantics deals with the meaning of sentences and words as fundamentals in the world.

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 [50], an ACM journal specific for that subject.

Computing semantic similarity based on novel models of semantic representation using Wikipedia

The protocol is a documentation of the review process and must have all the information needed to perform the literature review in a systematic way. The analysis of selected studies, which is performed in the data extraction phase, will provide the answers to the research questions that motivated the literature review. Kitchenham and Charters [3] present a very useful guideline for planning and conducting systematic literature reviews.

In other words, it shows how to put together entities, concepts, relations, and predicates to describe a situation. Moreover, QuestionPro might connect with other specialized semantic analysis tools or NLP platforms, depending on its integrations or APIs. This integration could enhance the analysis by leveraging more advanced semantic processing capabilities from external tools. Moreover, while these are just a few areas where the analysis finds significant applications. Its potential reaches into numerous other domains where understanding language’s meaning and context is crucial.

Two-concept perception

As systematic reviews follow a formal, well-defined, and documented protocol, they tend to be less biased and more reproducible than a regular literature review. In this step, raw text is transformed into some data representation format that can be used as input for the knowledge extraction algorithms. The activities performed in the pre-processing step are crucial for the success of the whole text mining process. The data representation must preserve the patterns hidden in the documents in a way that they can be discovered in the next step. In the pattern extraction step, the analyst applies a suitable algorithm to extract the hidden patterns.

These solutions can provide instantaneous and relevant solutions, autonomously and 24/7. Semantic analysis can also benefit SEO (search engine optimisation) by helping to decode the content of a users’ Google searches and to be able to offer optimised and correctly referenced content. The goal is to boost traffic, all while improving the relevance of results for the user. As such, semantic analysis helps position the content of a website based on a number of specific keywords (with expressions like “long tail” keywords) in order to multiply the available entry points to a certain page. The challenge of semantic analysis is understanding a message by interpreting its tone, meaning, emotions and sentiment. Today, this method reconciles humans and technology, proposing efficient solutions, notably when it comes to a brand’s customer service.

For example, perception of the text as a bag of paragraphs can be accounted by exactly the same model that works with words and sentences. In that way, hierarchical semantic structure of information representation, typical to human cognition9,150, can be accessed. Impossibility of factorization (7) known as entanglement103 is a property of a compound state (4) in which subsystems have potential for coordinated resolution of uncertainties.

Healthcare professionals can develop more efficient workflows with the help of natural language processing. During procedures, doctors can dictate their actions and notes to an app, which produces an accurate transcription. NLP can also scan patient documents to identify patients who would be best suited for certain clinical trials.

Latent Semantic Analysis & Sentiment Classification with Python

Nowadays, any person can create content in the web, either to share his/her opinion about some product or service or to report something that is taking place in his/her neighborhood. Companies, organizations, and researchers are aware of this fact, so they are increasingly interested in using this information in their favor. Some competitive advantages that business can gain from the analysis of social media texts are presented in [47–49]. The authors developed case studies demonstrating how text mining can be applied in social media intelligence. From our systematic mapping data, we found that Twitter is the most popular source of web texts and its posts are commonly used for sentiment analysis or event extraction. The second most used source is Wikipedia [73], which covers a wide range of subjects and has the advantage of presenting the same concept in different languages.

This challenge is a frequent roadblock for artificial intelligence (AI) initiatives that tackle language-intensive processes. The goal of NER is to extract and label these named entities to better understand the structure and meaning of the text. As illustrated earlier, the word “ring” is ambiguous, as it can refer to both a piece of jewelry worn on the finger and the sound of a bell. To disambiguate the word and select the most appropriate meaning based on the given context, we used the NLTK libraries and the Lesk algorithm. Analyzing the provided sentence, the most suitable interpretation of “ring” is a piece of jewelry worn on the finger. Now, let’s examine the output of the aforementioned code to verify if it correctly identified the intended meaning.

Towards Automatic Verb Acquisition from VerbNet for Spoken Dialog Processing

In the larger context, this enables agents to focus on the prioritization of urgent matters and deal with them on an immediate basis. It also shortens response time considerably, which keeps customers satisfied and happy. Semantic analysis helps fine-tune the search engine optimization (SEO) strategy by allowing companies to analyze and decode users’ searches.

• The letters directly above the single words show the parts of speech for each word (noun, verb and determiner).
• Google incorporated ‘semantic analysis’ into its framework by developing its tool to understand and improve user searches.
• All factors considered, Uber uses semantic analysis to analyze and address customer support tickets submitted by riders on the Uber platform.

Each element is designated a grammatical role, and the whole structure is processed to cut down on any confusion caused by ambiguous words having multiple meanings. It was surprising to find the high presence of the Chinese language among the studies. Chinese language is the second most cited language, and the HowNet, a Chinese-English knowledge database, is the third most applied external source in semantics-concerned text mining studies.

Moreover, it is also helpful to customers as the technology enhances the overall customer experience at different levels. 1 A simple search for “systematic review” on the Scopus database in June 2016 returned, by subject area, 130,546 Health Sciences documents (125,254 of them for Medicine) and only 5,539 Physical Sciences (1328 of them for Computer Science). The coverage of Scopus publications are balanced between semantic analysis of text Health Sciences (32% of total Scopus publication) and Physical Sciences (29% of total Scopus publication). In this study, we identified the languages that were mentioned in paper abstracts. We must note that English can be seen as a standard language in scientific publications; thus, papers whose results were tested only in English datasets may not mention the language, as examples, we can cite [51–56].

• For example, perception of the text as a bag of paragraphs can be accounted by exactly the same model that works with words and sentences.
• By leveraging TextBlob’s intuitive interface and powerful sentiment analysis capabilities, we can gain valuable insights into the sentiment of textual content.
• To store them all would require a huge database containing many words that actually have the same meaning.
• The idea of entity extraction is to identify named entities in text, such as names of people, companies, places, etc.