Semantic analysis allows organizations to interpret the meaning of the text and extract critical information from unstructured data. Semantic-enhanced machine learning tools are vital natural language processing components that boost decision-making and improve the overall customer experience. The process involves contextual text mining that identifies and extrudes subjective-type insight from various data sources. But, when analyzing the views expressed in social media, it is usually confined to mapping the essential sentiments and the count-based parameters. In other words, it is the step for a brand to explore what its target customers have on their minds about a business.
A system for semantic analysis determines the meaning of words in text. Semantics gives a deeper understanding of the text in sources such as a blog post, comments in a forum, documents, group chat applications, chatbots, etc.
These chatbots act as semantic analysis tools that are enabled with keyword recognition and conversational capabilities. These tools help resolve customer problems in minimal time, thereby increasing customer satisfaction. Moreover, granular insights derived from the text allow teams to identify the areas with loopholes and work on their improvement on priority. By using semantic analysis tools, concerned business stakeholders can improve decision-making and customer experience.
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. Wimalasuriya and Dou present a detailed literature review of ontology-based information extraction. The authors define the recent information extraction subfield, named ontology-based information extraction , identifying key characteristics of the OBIE systems that differentiate them from general information extraction systems. Bharathi and Venkatesan present a brief description of several studies that use external knowledge sources as background knowledge for document clustering. Reshadat and Feizi-Derakhshi present several semantic similarity measures based on external knowledge sources and a review of comparison results from previous studies. The goals of this paper were very similar to the other paper we examined about scientific taxonomies.
The authors present a chronological analysis from 1999 to 2009 of directed probabilistic topic models, such as probabilistic latent semantic analysis, latent Dirichlet allocation, and their extensions. It allows computers to understand and interpret sentences, paragraphs, or whole documents, by analyzing their grammatical structure, and identifying relationships between individual words in a particular context. Uber uses semantic analysis to analyze users’ satisfaction or dissatisfaction levels via social listening. This implies that whenever Uber releases an update or introduces new features via a new app version, the mobility service provider keeps track of social networks to understand user reviews and feelings on the latest app release.
Although several researches have been developed in the text mining field, the processing of text semantics remains an open research problem. The field lacks secondary studies in areas that has a high number of primary studies, such as feature enrichment for a better text representation in the vector space model. We found considerable differences in numbers of studies among different languages, since 71.4% of the identified studies deal with English and Chinese.
If you treat categories as ‘words’ and the skills used in each group as a ‘document’ (i.e, a list of words), then you could juse just about any text similarity or clustering algorithm. Latent Semantic Analysis, which is basically just SVD might be a good place to start.
— Brad Hackinen (@BradHackinen) November 11, 2022
The main difference between them is that in polysemy, the meanings of the words are related but in homonymy, the meanings of the words are not related. For example, if we talk about the same word “Bank”, we can write the meaning ‘a financial institution’ or ‘a river bank’. In that case it would be the example of homonym because the meanings are unrelated to each other. It may be defined as the words having same spelling or same form but having different and unrelated meaning. For example, the word “Bat” is a homonymy word because bat can be an implement to hit a ball or bat is a nocturnal flying mammal also.
With the help of meaning representation, we can link linguistic elements to non-linguistic elements. Insights derived from data also help teams detect areas of improvement and make better decisions. For example, you might decide to create a strong knowledge base by identifying the most common customer inquiries. The automated process of identifying in which sense is a word used according to its context.
When combined with machine learning, semantic analysis allows you to delve into your customer data by enabling machines to extract meaning from unstructured text at scale and in real time. For example, the word ‘Blackberry’ could refer to a fruit, a company, or its products, along with several other meanings. Moreover, context is equally important while processing the language, as it takes into account the environment of the sentence and then attributes the correct meaning to it. Semantic analysis helps in processing customer queries and understanding their meaning, thereby allowing an organization to understand the customer’s inclination. Moreover, analyzing customer reviews, feedback, or satisfaction surveys helps understand the overall customer experience by factoring in language tone, emotions, and even sentiments. Thus, semantic analysis involves a broader scope of purposes, as it deals with multiple aspects at the same time.
A novel semantic text analysis for product recommendation based on weighted product taxonomy based on customer behavior and navigational factors is proposed, and a heuristic algorithm to search product “watch” in weighted productTaxonomy is proposed. Categorizing products of an online retailer based on products’ titles using word2vec word-embedding and DBSCAN (density-based spatial clustering of applications with noise) clustering. Decomposition of lexical items like words, sub-words, affixes, etc. is performed in lexical semantics. Classification of lexical items like words, sub-words, affixes, etc. is performed in lexical semantics. The purpose of semantic analysis is to draw exact meaning, or you can say dictionary meaning from the text. What semantic annotation brings to the table are smart data pieces containing highly-structured and informative notes for machines to refer to.
The main characteristics of T/ DG’s Enterprise Search include the analysis of unstructured text using NLP processing techniques, semantic enrichment, image search’s deeper inclusion, and many more. https://t.co/KOmDtpFMAx #BigDataSolutions #BigData #DataSolutions #DataAnalytics pic.twitter.com/Dz8iw7jpGj
— The Digital Group (@thedigtalgroup) November 13, 2022
However, text mining is a wide research field and there is a lack of secondary studies that summarize and integrate the different approaches. 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. In the previous subsections, we presented the mapping regarding to each secondary research question. In this subsection, we present a consolidation of our results and point some future trends of semantics-concerned text mining. The second most used source is Wikipedia , which covers a wide range of subjects and has the advantage of presenting the same concept in different languages. Wikipedia concepts, as well as their links and categories, are also useful for enriching text representation [74–77] or classifying documents [78–80].
So, in this part of this series, we will start our discussion on Semantic analysis, which is a level of the NLP tasks, and see all the important terminologies or concepts in this analysis. With sentiment analysis, companies can gauge user intent, evaluate their experience, and accordingly plan on how to address their problems and execute advertising or marketing campaigns. In short, sentiment analysis can streamline and boost successful business strategies for enterprises.
However, creating this thesaurus would present another opportunity for our personal biases to affect the communities. Namely, a significant portion of the sources in our review took new data sets or subject areas and applied existing network science techniques to the semantic networks for more complex text categorization. Text classification and text clustering, as basic text mining tasks, are frequently applied in semantics-concerned text mining researches. Among other more specific tasks, sentiment analysis is a recent research field that is almost as applied as information retrieval and information extraction, which are more consolidated research areas. SentiWordNet, a lexical resource for sentiment analysis and opinion mining, is already among the most used external knowledge sources. Stavrianou et al. present a survey of semantic issues of text mining, which are originated from natural language particularities.
Even if the concept is still within its infancy stage, it has established its worthiness in boosting business analysis methodologies. The process involves various creative aspects and helps an organization to explore aspects that are usually impossible to extrude through manual analytical methods. The process is the most significant step towards handling and processing unstructured business data. Consequently, organizations can utilize the data resources that result from this process to gain the best insight into market conditions and customer behavior. To pull communities from the network, we decided to use Julia’s built-in label propagation function. Two flaws we encountered in the resultant communities were that the texts in the largest community didn’t seem related, with titles like “good”, “nice”, and “sucks” or “lovely product” and “average” together in the same community.