An OCR Pipeline and Semantic Text Analysis for Comics SpringerLink

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. Therefore, it is not a proper representation for all possible text mining applications. The use of Wikipedia is followed by the use of the Chinese-English knowledge database HowNet [82]. Finding HowNet as one of the most used external knowledge source it is not surprising, since Chinese is one of the most cited languages in the studies selected in this mapping (see the “Languages” section).

The lower number of studies in the year 2016 can be assigned to the fact that the last searches were conducted in February 2016. 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. Apart from these vital elements, the semantic analysis also uses semiotics and collocations to understand and interpret language. Semiotics refers to what the word means and also the meaning it evokes or communicates.

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Semantic analysis plays a vital role in the automated handling of customer grievances, managing customer support tickets, and dealing with chats and direct messages via chatbots or call bots, among other tasks. QuestionPro is survey software that lets users make, send out, and look at the results of surveys. Depending on how QuestionPro surveys are set up, the answers to those surveys could be used as input for an algorithm that can do semantic analysis. Grammatical analysis and the recognition of links between specific words in a given context enable computers to comprehend and interpret phrases, paragraphs, or even entire manuscripts. In Natural Language, the meaning of a word may vary as per its usage in sentences and the context of the text.

The semantic analysis uses two distinct techniques to obtain information from text or corpus of data. The first technique refers to text classification, while the second relates to text extractor. 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. As mentioned in the previous section, for the data analysis several corpora were used. For all the corpora, in the pre-processing phase we lemmatized the texts, we eliminated the hapax forms and we removed stop-words – articles, auxiliaries, conjunctions, pronouns and prepositions.

Relationship Extraction

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. Despite the fact that the user would have an important role in a real application of text mining methods, there is not much investment on user’s interaction in text mining research studies. A probable reason is the difficulty inherent to an evaluation based on the user’s needs. We also found some studies that use SentiWordNet [92], which is a lexical resource for sentiment analysis and opinion mining [93, 94].

Wikipedia concepts, as well as their links and categories, are also useful for enriching text representation [74–77] or classifying documents [78–80]. Today, machine learning algorithms and NLP (natural language processing) technologies are the motors of semantic analysis tools. 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. With lexical semantics, the study of word meanings, semantic analysis provides a deeper understanding of unstructured text. 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.

The researchers conducting the study must define its protocol, i.e., its research questions and the strategies for identification, selection of studies, and information extraction, as well as how the study results will be reported. The main parts of the protocol that guided the systematic mapping study reported in this paper are presented in the following. The “Method applied for systematic mapping” section presents an overview of systematic mapping method, since this is the type of literature review selected to develop this study and it is not widespread in the text mining community. In this section, we also present the protocol applied to conduct the systematic mapping study, including the research questions that guided this study and how it was conducted. The results of the systematic mapping, as well as identified future trends, are presented in the “Results and discussion” section.

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. The second most frequent identified application domain is the mining of web texts, comprising web pages, blogs, reviews, web forums, social medias, and email filtering [41–46]. The high interest in getting some knowledge from web texts can be justified by the large amount and diversity of text available and by the difficulty found in manual analysis.

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 is a subfield of Natural Language Processing (NLP) that attempts to understand the meaning of Natural Language. Understanding Natural Language might seem a straightforward process to us as humans. However, due to the vast complexity and subjectivity involved in human language, interpreting it is quite a complicated task for machines. Semantic Analysis of Natural Language captures the meaning of the given text while taking into account context, logical structuring of sentences and grammar roles.

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 application of text mining methods in information extraction of biomedical literature is reviewed by Winnenburg et al. [24]. 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. They also describe and compare biomedical search engines, in the context of information retrieval, literature retrieval, result processing, knowledge retrieval, semantic processing, and integration of external tools. The authors argue that search engines must also be able to find results that are indirectly related to the user’s keywords, considering the semantics and relationships between possible search results.

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. Text clustering is an unsupervised data mining problem for document organization and browsing, corpus summarization and document classification (Aggarwal & Zhai, 2012).

• The data representation must preserve the patterns hidden in the documents in a way that they can be discovered in the next step.
• This paper aims to point some directions to the reader who is interested in semantics-concerned text mining researches.
• However, the proposed solutions are normally developed for a specific domain or are language dependent.
• Thus, machines tend to represent the text in specific formats in order to interpret its meaning.

Depending on its usage, WordNet can also be seen as a thesaurus or a dictionary [64]. 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. Since 2019, Cdiscount has been using a semantic analysis solution to process all of its customer reviews online. This kind of system can detect priority axes of in place, based on post-purchase feedback.

Search engines will evaluate such texts as poor in quality and not show them on the first page. The analysis of the semantic core of a text assesses its keyword density, water and spamming. 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.

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. All factors considered, Uber uses semantic analysis to analyze and address customer support tickets submitted by riders on the Uber platform. The analysis can segregate tickets based on their content, such as map data-related issues, and deliver them to the respective teams to handle. The platform allows Uber to streamline and optimize the map data triggering the ticket. Moreover, granular insights derived from the text allow teams to identify the areas with loopholes and work on their improvement on priority.

Chatbots help customers immensely as they facilitate shipping, answer queries, and also offer personalized guidance and input on how to proceed further. Moreover, some chatbots are equipped with emotional intelligence that recognizes the tone of the language and hidden sentiments, framing emotionally-relevant responses to them. Maps are essential to Uber’s cab services of destination search, routing, and prediction of the estimated arrival time (ETA).

Thus, machines tend to represent the text in specific formats in order to interpret its meaning. This formal structure that is used to understand the meaning of a text is called meaning representation. Text for semantic analysis can be copied from the source code website and pasted into the field for analysis, or you can specify the site address and the analyzer will automatically load the content of the specified page.

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