Semantic Analysis in Action: Real-World Applications of AI-Based Text Understanding Techniques

You see, the word on its own matters less, and the words surrounding it matter more for the interpretation. A semantic analysis algorithm needs to be trained with a larger corpus of data to perform better. Patient monitoring involves tracking patient data over time, identifying trends, and alerting healthcare professionals to potential health issues. Drug discovery involves using semantic analysis to identify the most promising compounds for drug development.

And it represents semantic as whole and can be substituted among semantic modes. Advanced Concepts, Methods, and Applications in Semantic Computing is a scholarly reference book that provides a sound theoretical foundation for the application of semantic methods, concepts, and technologies for practical problem solving. It is designed as a comprehensive and reliable resource on how semantic-oriented approaches can be used to aid new emergent technologies and tackle real-world problems. NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models.

Start Using Sentiment Analysis in Business

The goal was to correlate Twitter user characteristics, such as their number of followers, amount of activity, and quantity of tweets, with the tone of the debates. The main focus was the relationship between the tweet’s sentiment and the hashtag topic that was included in the tweet. The tweet’s status and its author’s status are related to the sentiment analysis of the tweets. By doing this, it can reveal details about user demographics and how they act during a tumultuous election season. A pair of words can be synonymous in one context but may be not synonymous in other contexts under elements of semantic analysis. Homonymy refers to two or more lexical terms with the same spellings but completely distinct in meaning under elements of semantic analysis.

A method by combining LSA with SVM has been presented for protein remote homology detection. Various ‘words’ of ‘protein sequence language’ have been investigated, including N-grams (Leslie et al., 2002), patterns (Dong et al., 2005) and motifs (Ben-Hur and Brutlag, 2003). Experimental results showed that the use of LSA technology significantly improves the performance of protein remote homology detection.

Application of Latent Semantic Analysis in Accounting Research

The parameters of SVM are used by default of the Gist package except that the kernel function i.e. the radius basis function (RBF) kernel. This path of natural language processing focuses on identification of named entities such as persons, locations, organisations which are denoted by proper nouns. With nearly 1 in 5 respondents choosing to include information in the open text field, it is important to know their characteristics. Adjusted data interestingly suggest some weak patterns, albeit significant, in response to the open text field differentiated by sex, age, active-duty status, and combat occupations. Air Force personnel were least likely to include a meaningful response to the question, but were also most likely to respond and respond early to the initial invitation for enrollment [6, 12]. Combat specialists and Marine Corps members were also more likely to respond to the open text question, which may be attributable to the ongoing combat operations in Iraq and Afghanistan.

• Furthermore, it can determine similarity among responses without accounting for word order or even if passages share no words in common [22].
• The parameters of SVM are used by default of the Gist package except that the kernel function i.e. the radius basis function (RBF) kernel.
• This popular technique is used by businesses to identify and group client opinions regarding a certain good, service, or concept.
• Institutions like universities and colleges, as well as public and private libraries are another semantic search example where the solution is used to intelligently organize vast data.
• In this paper however, we present a readability checker which uses semantic information in addition.

Deep-dive analysis algorithms have made it feasible to comprehend aspects, traits, and attributes in addition to client sentiment toward a product. These insights help organizations tailor their offerings them appealing to their target market. For instance, when analysing customer feedback and menu preferences, food giants like Domino’s, KFC, Pizza Hut, and McDonald’s use sentiment analysis. Sentiment analysis is a data analytics methodology to determine the emotional state or the overtone of the data (textual/audio/video) as positive, negative or neutral. The text use cases can be categorized by emotion and opinion thanks to Machine Learning algorithms that use Natural Language Processing. For more profound, potent insights, sentiment analysis can be integrated with other Artificial Intelligence tools like Text Summarization.

Sentiment Analysis: Concept, Analysis and Applications

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