Analytics Blog

  Text analysis is a method to extract useful information from unstructured text in an intelligent and effective way. This strategy can be used by researchers and scholars to organize varied and disorganized information into a systematic format. Text analysis in documents is used to convert subjective details into numerical details. It’s safe to say that text analysis is a study method for decoding content and producing logical conclusions. Text analysis is used by researc h ers and scholars to create a relationship between different variables. When it comes to the commercial side of things, text analysis covers a wide range of topics, including semantic search and content management. Textual analysis is frequently used in research to analyze texts such as survey questionnaires and polls, as well as various types of media. Textual data is used by researchers to generate study evidence regarding interpersonal interaction. The following are some of the data collecting methods: Questionna

  Sentiment analysis  is simply the process of categorizing the sentiments underlying a text. It is such a simple task that it can also be done manually; simply read each piece of feedback and determine whether it is positive or negative. Among many analytical fields, one in which humans outperform all others is the ability to recognize feelings. However, for feedback presented to you, such as  40–50  or even  100 , this is doable. However, if you have a data set of, say,  10,000  reviews, manually analyzing them becomes impossible. Not to mention the time and bias that will occur. Wh i le data growth is unavoidable for any expanding business, the value of the data remains a function of analytical quality. BytesView  and other sentiment analysis tools are rapidly replacing traditional methods of polling the public, tracking brand and product reputation, analyzing customer experiences, and conducting market research. By examining and evaluating customer sentiments with such tools, brand

  The Text Analytics API  is a cloud-based system that includes content analysis, text mining, keyword density extraction, language detection, and named entity recognition among other Natural Language Processing. Text analysis APIs allow you to use the leverage of pre-built tools rather than having to create the software from scratch. Based on your skills, budget, and timeline, you can use open-source or  SaaS  software. While open-source software is free, adaptable, and provides a wealth of resources, using open-source APIs will necessitate a team of machine learning experts. SaaS tools, on the other hand, give users access to advanced text mining solutions that can be implemented quickly and easily with just a few lines of code and require no prior knowledge of application development or machine learning. Benefits of implementing Text analytics API. It simplifies your business processes. It enables knowledge workers to make the most of their time. It automates and automates time-cons

  Aspect-based sentiment analysis   is a text analysis technique that divides data into aspects and helps determine the sentiment associated with each component. When we talk about  aspects , we’re making reference to the features or elements of a product or service, such as “the customer experience,” “the time it takes to give a response to a complaint,” or “the convenience with which new software can be consolidated.” Aspect-based sentiment analysis is a crucial component for all businesses, as it necessitates them to listen to their consumers, understand their feelings, analyze their feedback, and focus on improving their  experiences , in addition to their expectations for your products and services. 1) Sentiments are positive or negative feelings about a specific topic. 2) Aspects are the category or feature that is being discussed. As the channels through which consumers can communicate their  emotions  expand, so does the value of aspect-based sentiment analysis. They are increa