Semantic Text Analysis for Detection of Compromised Accounts on Social Networks IEEE Conference Publication

Semantic analysis, a crucial component of NLP, empowers us to extract profound meaning and valuable insights from text data. By comprehending the intricate semantic relationships between words and phrases, we can unlock a wealth of information and significantly enhance a wide range of NLP applications. In this comprehensive article, we will embark on a captivating journey into the realm of semantic analysis. We will delve into its core concepts, explore powerful techniques, and demonstrate their practical implementation through illuminating code examples using the Python programming language. Get ready to unravel the power of semantic analysis and unlock the true potential of your text data.

• Researchers and practitioners are working to create more robust, context-aware, and culturally sensitive systems that tackle human language’s intricacies.
• Schiessl and Bräscher [20], the only identified review written in Portuguese, formally define the term ontology and discuss the automatic building of ontologies from texts.
• [8] [6] Our research is more similar to the work of Ravi since we also worked with raw text and examining it through k-grams.
• In conclusion, semantic analysis in NLP is at the forefront of technological innovation, driving a revolution in how we understand and interact with language.
• The Wolfram Language includes increasingly sophisticated tools for analyzing and visualizing text, both structurally and semantically.
• A probable reason is the difficulty inherent to an evaluation based on the user’s needs.

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. In the social sciences, textual analysis is often applied to texts such as interview transcripts and surveys, as well as to various types of media. Social scientists use textual data to draw empirical conclusions about social relations.

Sentiment Analysis

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. A company can scale up its customer communication by using semantic analysis-based tools. A general text mining process can be seen as a five-step process, as illustrated in Fig. The process starts with the specification of its objectives in the problem identification step.

Also, it can give you actionable insights to prioritize the product roadmap from a customer’s perspective. Google’s free visualization tool allows you to create interactive reports using a wide variety of data. Once you’ve imported your data you can use different tools to design your report and turn your data into an impressive visual story. Share the results with individuals or teams, publish them on the web, or embed them on your website. Extractors are sometimes evaluated by calculating the same standard performance metrics we have explained above for text classification, namely, accuracy, precision, recall, and F1 score.

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. Their attempts to categorize student reading comprehension relate to our goal of categorizing sentiment. 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. Our cutoff method allowed us to translate our kernel matrix into an adjacency matrix, and translate that into a semantic network. In this model, each document is represented by a vector whose dimensions correspond to features found in the corpus. Despite the good results achieved with a bag-of-words, this representation, based on independent words, cannot express word relationships, text syntax, or semantics.

Besides, Semantics Analysis is also widely employed to facilitate the processes of automated answering systems such as chatbots – that answer user queries without any human interventions. Semantic analysis also takes collocations (words that are habitually juxtaposed with each other) and semiotics (signs and symbols) into consideration while deriving meaning from text. Information Extraction is the name of the scientific discipline behind text mining. Turn strings to things with Ontotext’s free application for automating the conversion of messy string data into a knowledge graph. Unlock the potential for new intelligent public services and applications for Government, Defence Intelligence, etc.

A graphical representation shows which group a text belongs to and thus allows you to find texts that deal with related topics. It may offer functionalities to extract keywords or themes from textual responses, thereby aiding in understanding the primary topics or concepts discussed within the provided text. Indeed, discovering a chatbot capable of understanding emotional intent or a voice bot’s discerning tone might seem like a sci-fi concept. Semantic analysis, the engine behind these advancements, dives into the meaning embedded in semantic analysis of text the text, unraveling emotional nuances and intended messages. Expert.ai’s rule-based technology starts by reading all of the words within a piece of content to capture its real meaning. It then identifies the textual elements and assigns them to their logical and grammatical roles.

A probable reason is the difficulty inherent to an evaluation based on the user’s needs. Semantic analysis stands as the cornerstone in navigating the complexities of unstructured data, revolutionizing how computer science approaches language comprehension. Its prowess in both lexical semantics and syntactic analysis enables the extraction Chat GPT of invaluable insights from diverse sources. MonkeyLearn makes it simple for you to get started with automated semantic analysis tools. Using a low-code UI, you can create models to automatically analyze your text for semantics and perform techniques like sentiment and topic analysis, or keyword extraction, in just a few simple steps.

Why Is Semantic Analysis Important to NLP?

Classification corresponds to the task of finding a model from examples with known classes (labeled instances) in order to predict the classes of new examples. On the other hand, clustering is the task of grouping examples (whose classes are unknown) based on their similarities. Thus, the ability of a semantic analysis definition to overcome the ambiguity involved in identifying the meaning of a word based on its usage and context is called Word Sense Disambiguation.

When you search for a term on Google, have you ever wondered how it takes just seconds to pull up relevant results? Google’s algorithm breaks down unstructured data from web pages and groups pages into clusters around a set of similar words or n-grams (all possible combinations of adjacent words or letters in a text). So, the pages from the cluster that contain a higher count of words or n-grams relevant to the search query will appear first within the results.

Product Analytics

You might then turn to your keyboard, and type a SQL query that will select the book name(s) that contains all of the words “color, zebra, variations” and would order in terms of relevance. MonkeyLearn’s data visualization tools make it easy to understand your results in striking dashboards. Spot patterns, trends, and immediately actionable insights in broad strokes or minute detail. Every other concern – performance, scalability, logging, architecture, tools, etc. – is offloaded to the party responsible for maintaining the API.

You might apply this technique to analyze the words or expressions customers use most frequently in support conversations. For example, if the word ‘delivery’ appears most often in a set of negative support tickets, this might suggest customers are unhappy with your delivery service. Text extraction is another widely used text analysis technique that extracts pieces of data that already exist within any given text. You can extract things like keywords, prices, company names, and product specifications from news reports, product reviews, and more. In this guide, learn more about what text analysis is, how to perform text analysis using AI tools, and why it’s more important than ever to automatically analyze your text in real time. There is no other option than to secure a comprehensive engagement with your customers.

In the next section, we’ll explore the practical applications of semantic analysis across multiple domains. Semantic analysis allows advertisers to display ads that are contextually relevant to the content being consumed by users. This approach not only increases the chances of ad clicks but also enhances user experience by ensuring that ads align with the users’ interests.

In the next section, we’ll explore future trends and emerging directions in semantic analysis. There have also been huge advancements in machine translation through the rise of recurrent neural networks, about which I also wrote a blog post. Now, imagine all the English words in the vocabulary with all their different fixations at the end of them. To store them all would require a huge database containing many words that actually have the same meaning.

However, as our goal was to develop a general mapping of a broad field, our study differs from the procedure suggested by Kitchenham and Charters [3] in two ways. Firstly, Kitchenham and Charters [3] state that the systematic review should be performed by two or more researchers. Although our mapping study was planned by two researchers, the study selection and the information extraction phases were conducted by only one due to the resource constraints. In this semantic text analysis process, the other researchers reviewed the execution of each systematic mapping phase and their results. Secondly, systematic reviews usually are done based on primary studies only, nevertheless we have also accepted secondary studies (reviews or surveys) as we want an overview of all publications related to the theme. Moreover, semantic categories such as, ‘is the chairman of,’ ‘main branch located a’’, ‘stays at,’ and others connect the above entities.

In fact, it’s not too difficult as long as you make clever choices in terms of data structure. Semantic analysis, on the other hand, is crucial to achieving a high level of accuracy when analyzing text. Semantic analysis enables these systems to comprehend user queries, leading to more accurate responses and better conversational experiences.

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. 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 prototype enables easy and efficient algorithmic processing of large corpuses of documents and texts with finding content similarities using advanced grouping and visualisation. A web tool supporting natural language (like legislation, public tenders) is planned to be developed. The process enables computers to identify and make sense of documents, paragraphs, sentences, and words as a whole. Previous approaches to semantic analysis, specifically those which can be described as using templates, use several levels of representation to go from the syntactic parse level to the desired semantic representation. The different levels are largely motivated by the need to preserve context-sensitive constraints on the mappings of syntactic constituents to verb arguments.

Relationship extraction is a procedure used to determine the semantic relationship between words in a text. In semantic analysis, relationships include various entities, such as an individual’s name, place, company, designation, etc. Some common methods of analyzing texts in the social sciences include content analysis, thematic analysis, and discourse analysis.

A word cloud3 of methods and algorithms identified in this literature mapping is presented in Fig. 9, in which the font size reflects the frequency of the methods and algorithms among the accepted papers. You can foun additiona information about ai customer service and artificial intelligence and NLP. We can note that the most common approach deals with latent semantics through Latent Semantic Indexing (LSI) [2, 120], a method that can be used for data dimension reduction and that is also known as latent semantic analysis. The paper describes the state-of-the-art text mining approaches for supporting manual text annotation, such as ontology learning, named entity and concept identification.

Semantic analysis can be beneficial here because it is based on the whole context of the statement, not just the words used. Using Syntactic analysis, a computer would be able to understand the parts of speech of the different words in the sentence. Based on the understanding, it can then try and estimate the meaning of the sentence.

Several companies are using the sentiment analysis functionality to understand the voice of their customers, extract sentiments and emotions from text, and, in turn, derive actionable data from them. It helps capture the tone of customers when they post reviews and opinions on social media posts or company websites. Google incorporated ‘semantic analysis’ into its framework by developing its tool to understand and improve user searches. 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.

Most SaaS tools are simple plug-and-play solutions with no libraries to install and no new infrastructure. The permissive MIT license makes it attractive to businesses looking to develop proprietary models. It’s designed to enable rapid iteration and experimentation with deep neural networks, and as a Python library, it’s uniquely user-friendly. PyTorch is a deep learning platform built by Facebook and aimed specifically at deep learning. PyTorch is a Python-centric library, which allows you to define much of your neural network architecture in terms of Python code, and only internally deals with lower-level high-performance code. GlassDollar, a company that links founders to potential investors, is using text analysis to find the best quality matches.

In the “Systematic mapping summary and future trends” section, we present a consolidation of our results and point some gaps of both primary and secondary studies. In some cases, it gets difficult to assign a sentiment classification to a phrase. That’s where the natural language processing-based sentiment analysis comes in handy, as the algorithm makes an effort to mimic regular human language. Semantic video analysis & content search uses machine learning and natural language processing to make media clips easy to query, discover and retrieve. It can also extract and classify relevant information from within videos themselves. The majority of the semantic analysis stages presented apply to the process of data understanding.

Data-driven drug development promises to enable pharmaceutical companies to derive deeper insights and make faster, more informed decisions. A fundamental step to achieving this nirvana is important to be able to make sense of the information available and to make connections between disparate, heterogeneous data sources. This semantic enrichment opens up new possibilities for you to mine data more effectively, derive valuable insights and ensure you never miss something relevant. However, semantic analysis has challenges, including the complexities of language ambiguity, cross-cultural differences, and ethical considerations.

Now you know a variety of text analysis methods to break down your data, but what do you do with the results? Business intelligence (BI) and data visualization tools make it easy to understand your results in striking dashboards. The Naive Bayes family of algorithms is based on Bayes’s Theorem and the conditional probabilities of occurrence of the words of a sample text within the words of a set of texts that belong to a given tag.

Similarly, in the case of phonetic similarity between words, like the two spellings of the same name “ashlee” and “aishleigh”, the hamming similarity would not reflect that the words are essentially the same when spoken. One way we could address this limitation would be to add another similarity test based on a phonetic dictionary, to check for review titles that are the same idea, but misspelled through user error. Semantic analysis is the process of understanding the meaning and interpretation of words, signs and sentence structure. I say this partly because semantic analysis is one of the toughest parts of natural language processing and it’s not fully solved yet.

The semantic analysis does throw better results, but it also requires substantially more training and computation. Syntactic analysis involves analyzing the grammatical syntax of a sentence to understand its meaning. Natural language processing (NLP) is a field of artificial intelligence that focuses on creating interactions between computers and human language. It aims to facilitate communication between humans and machines by teaching computers to read, process, understand and perform actions based on natural language.

Businesses can win their target customers’ hearts only if they can match their expectations with the most relevant solutions. Hadoop systems can hold billions of data objects but suffer from the common problem that such objects can be hard or organise due to a lack of descriptive meta-data. SciBite can improve the discoverability of this vast resource by unlocking the knowledge held in unstructured text to power next-generation analytics and insight.

With the ongoing commitment to address challenges and embrace future trends, the journey of semantic analysis remains exciting and full of potential. Stanford CoreNLP is a suite of NLP tools that can perform tasks like part-of-speech tagging, named entity recognition, and dependency parsing. Semantics is the branch of linguistics that focuses on the meaning of words, phrases, and sentences within a language. It seeks to understand how words and combinations of words convey information, convey relationships, and express nuances.

We anticipate the emergence of more advanced pre-trained language models, further improvements in common sense reasoning, and the seamless integration of multimodal data analysis. As semantic analysis develops, its influence will extend beyond individual industries, fostering innovative solutions and enriching human-machine interactions. Transformers, developed by Hugging Face, is a library that provides easy access to state-of-the-art transformer-based NLP models. These models, including BERT, GPT-2, and T5, excel in various semantic analysis tasks and are accessible through the Transformers library.

Keyword and Theme Extraction:

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. Context plays a critical role in processing language as it helps to attribute the correct meaning.

Understanding these semantic analysis techniques is crucial for practitioners in NLP. The choice of method often depends on the specific task, data availability, and the trade-off between complexity and performance. This mapping is based on 1693 studies selected as described in the previous section.

It might be desired for an automated system to detect as many tickets as possible for a critical tag (for example tickets about ‘Outrages / Downtime’) at the expense of making some incorrect predictions along the way. In this case, making a prediction will help perform the initial routing and solve most of these critical issues ASAP. If the prediction is incorrect, the ticket will get rerouted by a member of the team. When processing thousands of tickets per week, high recall (with good levels of precision as well, of course) can save support teams a good deal of time and enable them to solve critical issues faster.

The purpose of Text Analysis is to create structured data out of free text content. The process can be thought of as slicing and dicing heaps of unstructured, heterogeneous documents into easy-to-manage and interpret data pieces. Text Analysis is close to other terms like Text Mining, Text Analytics and Information Extraction – see discussion below. Interlink your organization’s data and content by using knowledge graph powered natural language processing with our Content Management solutions.

In the future, we plan to improve the user interface for it to become more user-friendly. And it is when Text Analysis “prepares” the content, that Text Analytics kicks in to help make sense of these data. Achieving high accuracy for a specific domain and document types require the development of a customized text mining pipeline, which incorporates or reflects these specifics. With the help of meaning representation, unambiguous, canonical forms can be represented at the lexical level. Similarity from the WordNet perspective can be implemented using the concept of “word distance”.

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. The distribution of text mining tasks identified in this literature mapping is presented in Fig.

• When considering semantics-concerned text mining, we believe that this lack can be filled with the development of good knowledge bases and natural language processing methods specific for these languages.
• To overcome the ambiguity of human language and achieve high accuracy for a specific domain, TA requires the development of customized text mining pipelines.
• For example, in customer reviews on a hotel booking website, the words ‘air’ and ‘conditioning’ are more likely to co-occur rather than appear individually.
• Depending on which concepts appear in several texts at the same time, it reveals the relatedness between them and, according to this criterion, determines groups and classifies the texts among them.

The text mining analyst, preferably working along with a domain expert, must delimit the text mining application scope, including the text collection that will be mined and how the result will be used. Semantic analysis methods will provide companies the ability to understand the meaning of the text and achieve comprehension and communication levels that are at par with humans. All factors considered, Uber uses semantic analysis to analyze and address customer support tickets submitted by riders on the Uber platform. The semantic analyser scans the texts in a collection and extracts characteristic concepts from them. Depending on which concepts appear in several texts at the same time, it reveals the relatedness between them and, according to this criterion, determines groups and classifies the texts among them. The characteristic concepts of each group can be used to give a quick overview of the content covered in each collection.

In other words, it shows how to put together entities, concepts, relations, and predicates to describe a situation. In conclusion, semantic analysis in NLP is at the forefront of technological innovation, driving a revolution in how we understand and interact with language. It promises to reshape our world, making communication more accessible, efficient, and meaningful.

Bos [31] presents an extensive survey of computational semantics, a research area focused on computationally understanding human language in written or spoken form. You can foun additiona information about ai customer service and artificial intelligence and NLP. 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. The author also discusses the generation of background knowledge, which can support reasoning tasks. The authors present an overview of relevant aspects in textual entailment, discussing four PASCAL Recognising Textual Entailment (RTE) Challenges.

They declared that the systems submitted to those challenges use cross-pair similarity measures, machine learning, and logical inference. Reshadat and Feizi-Derakhshi [19] present several semantic similarity measures based on external knowledge sources (specially WordNet and MeSH) and a review of comparison results from previous studies. Besides the top 2 application domains, other domains that show up in our mapping refers to the mining of specific types of texts. We found research studies in mining news, scientific papers corpora, patents, and texts with economic and financial content.

Grobelnik [14] also presents the levels of text representations, that differ from each other by the complexity of processing and expressiveness. The most simple level is the lexical level, which includes the common bag-of-words and n-grams representations. Systematic mapping studies follow an well-defined protocol as in any systematic review.

Companies use text analysis tools to quickly digest online data and documents, and transform them into actionable insights. Right

now, sentiment analytics is an emerging

trend in the business domain, and it can be used by businesses of all types and

sizes. 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.

Semantics refers to the study of meaning in language and is at the core of NLP, as it goes beyond the surface structure of words and sentences to reveal the true essence of communication. Semantic analysis starts with lexical semantics, which studies individual words’ meanings (i.e., dictionary definitions). Semantic analysis then examines relationships between individual words and analyzes the https://chat.openai.com/ meaning of words that come together to form a sentence. The formal semantics defined by Sheth et al. [28] is commonly represented by description logics, a formalism for knowledge representation. 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.

Besides that, users are also requested to manually annotate or provide a few labeled data [166, 167] or generate of hand-crafted rules [168, 169]. The advantage of a systematic literature review is that the protocol clearly specifies its bias, since the review process is well-defined. However, it is possible to conduct it in a controlled and well-defined way through a systematic process. Search engines use semantic analysis to understand better and analyze user intent as they search for information on the web.

Dandelion API is a set of semantic APIs to extract meaning and insights from texts in several languages (Italian, English, French, German and Portuguese). It’s optimized to perform text mining and text analytics for short texts, such as tweets and other social media. Dandelion API extracts entities (such as persons, places and events), categorizes and classifies documents in user-defined categories, augments the text with tags and links to external knowledge graphs and more. This mapping shows that there is a lack of studies considering languages other than English or Chinese. The low number of studies considering other languages suggests that there is a need for construction or expansion of language-specific resources (as discussed in “External knowledge sources” section). These resources can be used for enrichment of texts and for the development of language specific methods, based on natural language processing.

Key aspects of lexical semantics include identifying word senses, synonyms, antonyms, hyponyms, hypernyms, and morphology. In the next step, individual words can be combined into a sentence and parsed to establish relationships, understand syntactic structure, and provide meaning. By incorporating semantic analysis, AI systems can better understand the nuances and complexities of human language, such as idioms, metaphors, and sarcasm. This has opened up new possibilities for AI applications in various industries, including customer service, healthcare, and finance. WordNet can be used to create or expand the current set of features for subsequent text classification or clustering.

5) This is where we will need some programming expertise and lots of computational resources. If you would like to give text analysis a go, sign up to MonkeyLearn for free and begin training your very own text classifiers and extractors – no coding needed thanks to our user-friendly interface and integrations. A Short Introduction to the Caret Package shows you how to train and visualize a simple model.

Since reviewing many documents and selecting the most relevant ones is a time-consuming task, we have developed an AI-based approach for the content-based review of large collections of texts. The approach of semantic analysis of texts and the comparison of content relatedness between individual texts in a collection allows for timesaving and the comprehensive analysis of collections. 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. Semantic analysis aids search engines in comprehending user queries more effectively, consequently retrieving more relevant results by considering the meaning of words, phrases, and context. While, as humans, it is pretty simple for us to understand the meaning of textual information, it is not so in the case of machines.

The conduction of this systematic mapping followed the protocol presented in the last subsection and is illustrated in Fig. The selection and the information extraction phases were performed with support of the Start tool [13]. Understanding these terms is crucial to NLP programs that seek to draw insight from textual information, extract information and provide data. It is also essential for automated processing and question-answer systems like chatbots. Beyond just understanding words, it deciphers complex customer inquiries, unraveling the intent behind user searches and guiding customer service teams towards more effective responses.

As semantic analysis advances, it will profoundly impact various industries, from healthcare and finance to education and customer service. Cross-lingual semantic analysis will continue improving, enabling systems to translate and understand content in multiple languages seamlessly. Despite these challenges, we at A L G O R I S T are continually working to overcome these drawbacks and improve the accuracy, efficiency, and applicability of semantic analysis techniques. Careful consideration of these limitations is essential when incorporating semantic analysis into various applications to ensure that the benefits outweigh the potential drawbacks. Less than 1% of the studies that were accepted in the first mapping cycle presented information about requiring some sort of user’s interaction in their abstract.