The vast amount of information created on the internet every day can be useful in business and academia. For example, ordinary conversations on social networks, product reviews, and customer feedback provide data in form of text. The large amount and qualitative nature of this data, however, makes it difficult to utilize. Unlike quantitative data which can be easily coded and analyzed, qualitative data is more difficult to gather, code and analyze. More sophisticated methods and advanced tools of text analysis are required for this purpose. This is why online text analysis tools are becoming increasingly important in today’s fast-paced digital world.
What is online text analysis?
Text analysis is a process of extracting useful high-quality information from unstructured data written/pasted as text, uploaded through text files such Word and PDF, or through links from websites. Text analysis helps to highlight important terms and phrases and allows you to categorize open-ended responses. Ultimately, it makes research easier and faster.
Online text analysis helps to unearth patterns and trends in the text, or combinations of relevance that draw various meanings for the researcher. Through these derivatives, one can gain insight into the attitudes, concerns, behaviors, motivations and intentions of the respondents. This is particularly useful to qualitative researchers such as social science scholars. Online text analysis is also useful in optimizing customer experience.
Online text analysis tools
There are many text analytics tools available online today, for example Voyant Tools. Basically, these tools provide detailed statistics of text stored online. They enable the user to determine the frequency of a word or phrase, create concordances, and view words in context. The patterns produced are easy to study and understand. Some online text analysis tools generate interactive word clouds to facilitate keyword searching, exploring relationships between keywords and the other content and generating a summary of passages that contain the keyword in question.
Online text analytics involve data mining and the use of AI algorithms to produce meaningful and easy-to-understand visual results. Such results can be used by anyone for further analysis. You don’t have to be a data scientist or statistician to use the results. Only some quick-to-learn basics are required.
Users of online text analysis tools
Advancements in technology and the internet have completely changed the way research is done. Digital methods of data collection are preferred to manual methods. This has led to the rising popularity of online surveys, online customer feedback forms, pop-up review messages on interactive sites and so on. It is likely that every active internet user employs some elements of text analytics. Below are various ways in which online text analysis tools are used:
1. Marketing
Online marketing requires the use of tools such as sentiment analysis tools. These are online text analysis tools that help to predict trends based on users’ attitudes. Marketers use sentiment analysis to learn how products are being perceived in the market. Text analytics also analyze subtle text patterns that answer various questions about customer performance and this helps to increase marketing effectiveness.
2. Indexing
Organizations that deal with search and indexing find online text analytics very useful. They are able to automate the text mining and analysis process as well as improve their results.
3. Academic research
Text mining is of critical importance in research for disciplines in which most information is contained within written text in digital formats. There is no quicker and easier way to utilize such data than by using online text analysis tools, such as Edusson Help Guides.
4. Blogging and news editing
Writers, bloggers, and editors benefit from online text analytics by having the ability to summarize, associate and package content from across many sources thus increasing their chances of monetizing the content.
Anything that makes research more convenient sounds like a good idea. Now days it’s extremely easy to find data, but sorting through the data has become a challenge. I like your point that text analysis can help with academic research in particular.