A Survey of Semantic Analysis Approaches SpringerLink

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. It is normally based on external knowledge sources and can also be based on machine learning methods [36, 130–133]. In semantic analysis, word sense disambiguation refers to an automated process of determining the sense or meaning of the word in a given context.

This practice, known as “social listening,” involves gauging user satisfaction or dissatisfaction through social media channels. Semantic analysis enables these systems to comprehend user queries, leading to more accurate responses and better conversational experiences. This is often accomplished by locating and extracting the key ideas and connections found in the text utilizing algorithms and AI approaches. Every type of communication — be it a tweet, LinkedIn post, or review in the comments section of a website — may contain potentially relevant and even valuable information that companies must capture and understand to stay ahead of their competition. Capturing the information is the easy part but understanding what is being said (and doing this at scale) is a whole different story. For Example, you could analyze the keywords in a bunch of tweets that have been categorized as “negative” and detect which words or topics are mentioned most often.

References:

There are also surveys about the techniques of semantic similarity measurement between words (Elavarasi et al., 2014, Soleimandarabi et al., 2015, Zhang et al., 2012). Moreover, there is a discussion about types of semantic relationships between words on the textual data of the social networks (Irfan et al., 2015). Similar to our topic, there are surveys on semantic document clustering such as Naik, Prajapati, and Dabhi (2015) and Saiyad, Prajapati, and Dabhi (2016).

The selection and the information extraction phases were performed with support of the Start tool [13]. In the following subsections, we describe our systematic mapping protocol and how this study was conducted. Besides, going even deeper in the interpretation of the sentences, we can understand their meaning—they are related to some takeover—and we can, for example, infer that there will be some impacts on the business environment. All in all, semantic analysis enables chatbots to focus on user needs and address their queries in lesser time and lower cost. In the ever-expanding era of textual information, it is important for organizations to draw insights from such data to fuel businesses. Semantic Analysis helps machines interpret the meaning of texts and extract useful information, thus providing invaluable data while reducing manual efforts.

This ends our Part-9 of the Blog Series on Natural Language Processing!

Further, digitised messages, received by a chatbot, on a social network or via email, can be analyzed in real-time by machines, improving employee productivity. In Table A1, we list the mapping between label index and label name, for each datasets examined in the main experimental evaluation. These corpora extend our evaluation to datasets with few classes and small number of samples (as is the case with BBC) and to datasets from a radically different domain (i.e., the medical content of Ohsumed).

In contrast to existing surveys, this survey strives to concentrate and address all the above-mentioned deficiencies by presenting a focused and deeply detailed literature review on the application of semantic 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. Semantic analysis within the framework of natural language processing evaluates and represents human language and analyzes texts written in the English language and other natural languages with the interpretation similar to those of human beings. The overall results of the study were that semantics is paramount in processing natural languages and aid in machine learning. This study has covered various aspects including the Natural Language Processing (NLP), Latent Semantic Analysis (LSA), Explicit Semantic Analysis (ESA), and Sentiment Analysis (SA) in different sections of this study.

As natural language consists of words with several meanings (polysemic), the objective here is to recognize the correct meaning based on its use. One can train machines to make near-accurate predictions by providing text samples as input to semantically-enhanced ML algorithms. Machine learning-based semantic analysis involves sub-tasks such as relationship extraction and word sense disambiguation. We observe that our approach performs the best for both the additional datasets, with the difference being less noticeable on the BBC dataset. The lexical-only word2vec pre-trained embeddings outperform both sense-based approaches, out of which SensEmbed achieves the highest accuracy. Retrofitting word2vec vectors improves the classification results to a minor extent over the Ohsumed dataset.

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. Semantic analysis refers to a process of understanding natural language (text) by extracting insightful information such as context, emotions, and sentiments from unstructured data. It gives computers and systems the ability to understand, interpret, and derive meanings from sentences, paragraphs, reports, registers, files, or any document of a similar kind.

Approach

In 2006, Strube & Ponzetto demonstrated that Wikipedia could be used in semantic analytic calculations.[2] The usage of a large knowledge base like Wikipedia allows for an increase in both the accuracy and applicability of semantic analytics. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. ArXiv is committed to these values and only works with partners that adhere to them. D This notation is also the one employed by the WordNet interface used in our semantic extraction process, the description of which follows. In light of the experimental results, we revisit the research questions stated in the introduction of the paper.

The context of each synset is tokenized into words, with each word mapped to a vector representation via the learned embedding matrix. The synset vector is the centroid produced by averaging all context word embeddings. Pairing QuestionPro’s survey features with specialized semantic analysis tools or NLP platforms allows for a deeper understanding of survey text data, yielding profound insights for improved decision-making. It’s not just about understanding text; it’s about inferring intent, unraveling emotions, and enabling machines to interpret human communication with remarkable accuracy and depth. From optimizing data-driven strategies to refining automated processes, semantic analysis serves as the backbone, transforming how machines comprehend language and enhancing human-technology interactions. This degree of language understanding can help companies automate even the most complex language-intensive processes and, in doing so, transform the way they do business.

Cross-language explicit semantic analysis (CL-ESA) is a multilingual generalization of ESA.[9] CL-ESA exploits a document-aligned multilingual reference collection (e.g., again, Wikipedia) to represent a document as a language-independent concept vector. The relatedness of two documents in different languages is assessed by the cosine similarity between the corresponding vector representations. Besides the vector space model, there are text representations based on networks (or graphs), which can make use of some text semantic features. 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]. A systematic review is performed in order to answer a research question and must follow a defined protocol.

• In the pattern extraction step, the analyst applies a suitable algorithm to extract the hidden patterns.
• We also expand our investigation to additional semantic extraction and disambiguation approaches, by considering the effect of the n-th degree hypernymy relations and of several context semantic embedding methods.
• Grobelnik [14] also presents the levels of text representations, that differ from each other by the complexity of processing and expressiveness.
• In semantic analysis with machine learning, computers use word sense disambiguation to determine which meaning is correct in the given context.
• The review reported in this paper is the result of a systematic mapping study, which is a particular type of systematic literature review [3, 4].
• However, there is a lack of secondary studies that consolidate these researches.

This corpus consists of 11,314 and 7532 training and test instances of user USENET posts, spanning 20 categories (or “newsgroups”) that pertain to different discussion topics (e.g., alt.atheism, sci.space, rec.sport.hockey, comp.graphics, etc.). The number of instances per class varies from 377 to 600 for the training set, and from 251 to 399 for the test set, while the mean number of words is 191 and 172 per training and test document, respectively. We use the “bydate” version, in which the train and test samples are separated in time (i.e., the train and the test set instances are posted before and after a specific date).

Read on to find out more about this semantic analysis and its applications for customer service. In this section, we outline the experiments performed to evaluate our semantic augmentation approaches for text classification. In Section 4.1, we describe the datasets and the experimental setup, in Section 4.2, we present and discuss the obtained results, and in Section 4.3, we compare our approach to related studies. Since WordNet represents a graph of interconnected synsets, we can exploit meaningful semantic connections to activate relevant neighboring synsets among the candidate ones.

This allows Cdiscount to focus on improving by studying consumer reviews and detecting their satisfaction or dissatisfaction with the company’s products. 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.

These visualizations help identify trends or patterns within the unstructured text data, supporting the interpretation of semantic aspects to some extent. It helps understand the true meaning of words, phrases, and sentences, leading to a more accurate interpretation of 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 the text, unraveling emotional nuances and intended messages. However, many organizations struggle to capitalize on it because of their inability to analyze unstructured data. This challenge is a frequent roadblock for artificial intelligence (AI) initiatives that tackle language-intensive processes.

• The tool analyzes every user interaction with the ecommerce site to determine their intentions and thereby offers results inclined to those intentions.
• Indeed, semantic analysis is pivotal, fostering better user experiences and enabling more efficient information retrieval and processing.
• It also allows traversal of the WordNet graph via the synset relation links mentioned above.
• You can proactively get ahead of NLP problems by improving machine language understanding.
• These proposed solutions are more precise and help to accelerate resolution times.

This field of research combines text analytics and Semantic Web technologies like RDF. We now delve into our approach for introducing external semantic information into the neural model. We present the textual (raw text) component of our learning pipeline in Section 3.1, the semantic component in Section 3.2, and the training process text semantic analysis that builds the classification model in Section 3.3. Latent Semantic Analysis (LSA) is a popular, dimensionality-reduction techniques that follows the same method as Singular Value Decomposition. LSA ultimately reformulates text data in terms of r latent (i.e. hidden) features, where r is less than m, the number of terms in the data.

Also, ‘smart search‘ is another functionality that one can integrate with ecommerce search tools. The tool analyzes every user interaction with the ecommerce site to determine their intentions and thereby offers results inclined to those intentions. 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.

Let’s say that there are articles strongly belonging to each category, some that are in two and some that belong to all 3 categories. We could plot a table where each row is a different document (a news article) and each column is a different topic. In the cells we would have a different numbers that indicated how strongly that document belonged to the particular topic (see Figure 3). Uber strategically analyzes user sentiments by closely monitoring social networks when rolling out new app versions.