Text Analysis Methods for Semantic Search and Query Expansion

This paper proposes an approach on a method for visual text analytics to support knowledge building, analytical reasoning and explorative analysis. For this purpose we use semantic network models that are automatically retrieved from unstructured text data using a parametric k-next-neighborhood model. Semantic networks are analyzed with methods of network analysis to gain quantitative and qualitative insights.

After deciding on k-grams, the next functions we implemented were similarity functions to assess similarity of different data set entries. Initially, we didn’t consider that our similarity function would need to examine vectorized strings instead of the string literals from the data set. Our first implementation to calculate similarity was a type of edit distance function which compared two strings based on characterto-character difference. After testing, this similarity function worked to precisely calculate the similarity of strings through one-grams/characters, but was not useful in our ultimate goal of comparing vectorized strings by k-grams.

Systematic mapping summary and future trends

Semantic analytics, also termed semantic relatedness, is the use of ontologies to analyze content in web resources. This field of research combines text analytics and Semantic Web technologies like RDF. In simple words, we can say that lexical semantics represents the relationship between lexical items, the meaning of sentences, and the syntax of the sentence. It is the first part of semantic analysis, in which we study the meaning of individual words. It involves words, sub-words, affixes (sub-units), compound words, and phrases also. QuestionPro is survey software that lets users make, send out, and look at the results of surveys.

Stavrianou et al. [15] present a survey of semantic issues of text mining, which are originated from natural language particularities. This is a good survey focused on a linguistic point of view, rather than focusing only on statistics. The authors discuss a series of questions concerning natural language issues that should be considered when applying the text mining process. Most of the questions are related to text pre-processing and the authors present the impacts of performing or not some pre-processing activities, such as stopwords removal, stemming, word sense disambiguation, and tagging. The authors also discuss some existing text representation approaches in terms of features, representation model, and application task. The set of different approaches to measure the similarity between documents is also presented, categorizing the similarity measures by type (statistical or semantic) and by unit (words, phrases, vectors, or hierarchies).

Sentiment Analysis

This paper focused on text mining German climate actions plans to see patterns in the text networks. In the experiment, three thesauri described categories, then the researchers ranked these categories by their perceived network importance. This type of analysis is very similar to our experiments, since the researchers categorized sentiments in the climate action plans. An ontology also played a key role in this paper, when they translated a vector space model of “document-section-termmatrices” into “document-category-term-matrices” through relations to the ontological categories. Therefore, this paper showed the importance of matrices and models to determine links in a text analysis network. The researchers were able to highlight improvement areas in the climate action plans, including suggesting more renewable resources in

the heat and mobility sectors.

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. Another solution would be to create a second knowledge base in the form of a thesaurus, with categories based on the type of one word judgements we see in the largest communities, like “good”, “nice”, and “bad”. This would allow us to categorize one-word titles more precisely, based on sentiment categories.

DSL Based Automatic Generation of Q&A Systems

Instead, the researchers simultaneously partitioned the rows and columns of matrices to create “co-clusters”, and use a two-mode matrix in the place of the common space-vector model. As a result, their new method for community detection considered the texts and words simultaneously, both in the rows and columns of the affiliation matrices. They concluded that the co-clustering approach avoided the mean value convergence and therefore mirrored real data more closely. We included this research because of its innovative use of the matrix for text analysis, and because they focused on mirroring patterns in real text data. Since we worked with user-inputted review titles, our dataset may show patterns unique to natural language text.

In this article, you will learn about some of the best text analysis methods for semantic search and query expansion, and how they can improve your search engine performance and user experience. A detailed literature review, as the review of Wimalasuriya and Dou [17] (described in “Surveys” section), would be worthy for organization and summarization of these specific research subjects. You are right saying that standards promoted by W3C (RDF/OWL/SPARQL and others) are great blue prints. Semantic Web technologies which implements these standards are not longer reserved to early adopters and are becoming main stream with RDF & SPARQL end points driven by powerful RDF triple stores.

Language Support

More precisely, we are using a fully convolutional network approach inspired by the U-Net architecture, combined with a VGG-16 based encoder. The trained model delivers state-of-the-art performance with an F1-score of over 0.94. Qualitative results suggest that wiggly tails, curved corners, and even illusory contours do not pose a major problem. Furthermore, the model has learned to distinguish speech balloons from captions.

• Textable is an open-source add-on bringing advanced text-analytical functionalities to the Orange Canvas data mining software package.
• Instead, the researchers simultaneously partitioned the rows and columns of matrices to create “co-clusters”, and use a two-mode matrix in the place of the common space-vector model.
• 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.
• Use text analytics to gain insights into customer and user behavior, analyze trends in social media and e-commerce, find the root causes of problems and more.
• Relationship extraction is a procedure used to determine the semantic relationship between words in a text.
• Text classification and text clustering, as basic text mining tasks, are frequently applied in semantics-concerned text mining researches.

The argument here is that in ordinary discourse a speech act’s meaning consists of an unintentional, taken-for-granted component plus an intentional, asserted component. The ensuing discussion reveals a structure of linguistic ambiguity within ordinary discourse by showing that descriptive utterances admit of semantic opposites. With the rise in machine learning and artificial intelligence approaches to big data, systems that can integrate into the complex ecosystem typically found within large enterprises are increasingly important.

Sentiment Analysis with Machine Learning

It is normally based on external knowledge sources and can also be based on machine learning methods [36, 130–133]. When the field of interest is broad and the objective is to have an overview of what is being developed in the research field, it is recommended to apply a particular type of systematic review named systematic mapping study [3, 4]. Systematic mapping studies follow an well-defined protocol as in any systematic review. The main differences between a traditional systematic review and a systematic mapping are their breadth and depth.

Semantic analysis tech is highly beneficial for the customer service department of any company. Moreover, it is also helpful to customers as the technology enhances the overall customer experience at different levels. In the second part, the individual words will be combined to provide meaning in sentences. Manual annotation, and sometimes generation of vocabularies etc., is too expensive and cumbersome without automated methods, i.e., text mining. The best process is often a hybrid one that combines automatic methods with manual curation, but this is still overall a text-analytics process.

DATAVERSITY Education

Text mining initiatives can get some advantage by using external sources of knowledge. Thesauruses, taxonomies, ontologies, and semantic networks are knowledge sources that are commonly used by the text mining community. Semantic networks is a network whose nodes are concepts that are linked by semantic relations. The most popular example is the WordNet [63], an electronic lexical database developed at the Princeton University. Depending on its usage, WordNet can also be seen as a thesaurus or a dictionary [64].

Also, some of the technologies out there only make you think they understand the meaning of a text. Understanding human language is considered metadialog.com a difficult task due to its complexity. For example, there are an infinite number of different ways to arrange words in a sentence.

Part 9: Step by Step Guide to Master NLP – Semantic Analysis

Also, words can have several meanings and contextual information is necessary to correctly interpret sentences. Just take a look at the following newspaper headline “The Pope’s baby steps on gays.” This sentence clearly has two very different interpretations, which is a pretty good example of the challenges in natural language processing. Since roughly 80% of data in the world resides in an unstructured format (link resides outside ibm.com), text mining is an extremely valuable practice within organizations.

Users can search large audio catalogs for the exact content they want without any manual tagging. SVACS provides customer service teams, podcast producers, marketing departments, and heads of sales, the power to search audio files by specific topics, themes, and entities. It automatically annotates your podcast data with semantic analysis information without any additional training requirements. Text Analytics Toolbox™ provides algorithms and visualizations for preprocessing, analyzing, and modeling text data. Models created with the toolbox can be used in applications such as sentiment analysis, predictive maintenance, and topic modeling.

Thus, the ability of a machine to overcome the ambiguity involved in identifying the meaning of a word based on its usage and context is called Word Sense Disambiguation. 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.

• It is also a key component of several machine learning tools available today, such as search engines, chatbots, and text analysis software.
• Looking at the languages addressed in the studies, we found that there is a lack of studies specific to languages other than English or Chinese.
• Text mining techniques have become essential for supporting knowledge discovery as the volume and variety of digital text documents have increased, either in social networks and the Web or inside organizations.
• They state that ontology population task seems to be easier than learning ontology schema tasks.
• Moreover, the system can prioritize or flag urgent requests and route them to the respective customer service teams for immediate action with semantic analysis.
• If combined with machine learning, semantic analysis lets you dig deeper into your data by making it possible for machines to pull purpose from an unstructured text at scale and in real time.

Popular algorithms for stemming include the Porter stemming algorithm from 1979, which still works well. Visualize your textual data flowing through the pipeline of your CRM or ERP system by integrating our text analysis tool. For on-premise systems that need the low-latency, high-speed integration of an SDK, Rosette Java is the way to go. It has been deployed in the most demanding, high-transaction environments, including web search engines, financial compliance, and border security.

• Schiessl and Bräscher [20] and Cimiano et al. [21] review the automatic construction of ontologies.
• Since roughly 80% of data in the world resides in an unstructured format (link resides outside ibm.com), text mining is an extremely valuable practice within organizations.
• In our work, we focused on semantic text analysis using a network science approach.
• SentiWordNet, a lexical resource for sentiment analysis and opinion mining, is already among the most used external knowledge sources.
• Gain a deeper understanding of the relationships between products and your consumers’ intent.
• 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.