There are billions of websites with countless texts on the Internet. This makes it difficult to keep track of them. Text classification is a method that provides an overall view and structures the offer. Which application areas are there for text classifications in the World Wide Web?
The amount of data on the Internet is so large that filtering by human experts alone is impossible to conceive. The more information is spread on the Internet, mainly in text form, the greater the need for machine analysis, sorting and classification. Examples:
Machine support is an effective aid for classifying texts. Artificial intelligence plays an increasingly important role here.
Artificial intelligence shows that it is also useful in the classification of texts. In this case, the knowledge acquisition of the algorithms is based on training data that are already pre-classified. New text documents are gradually compared with these training data. The principle of trial and error provides increasingly accurate results.
The problem with the analysis of words lies mostly in filtering out the irrelevant features. One approach for this is so-called stemming – each word is systematically traced back to the root of the word. By excluding superfluous features, the runtime of the programs is considerably reduced.
When classifying texts, not the meaning of individual words ultimately matters, but the context in which they are used.
For example: Even if the word flower does not appear in a text, the text nevertheless deals with the topic if words relating to the environment are used frequently, for example roses, tulips, garden or fertilizer.
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Obviously, any machine text classification has a certain probability of errors. The higher the probability of an appropriate classification, the better the underlying algorithm.
The complexity of a text document is an important factor for the classification of documents. How complex is a text? There are some indications. These are for example:
The classification of texts in terms of complexity offers added value in particular for Internet portals, which provide their visitors with a target group-specific range of links. The text classification also helps meeting different requirements, for example with regard to
In this respect, text classification is an efficient means of preserving the coherent style of a portal even when integrating external sources.
An important application of text classification is sentiment analysis. Sentiment analysis is a sub-area of text mining.
Text mining uses algorithms to filter out the core information from unstructured texts. In the (utopian) ideal case, this type of algorithm represents the intellectual process of human reading.
A sentiment analysis reveals whether a text (e.g. an evaluation comment or a post in social networks) has an overall positive or negative basic tendency – solely on the basis of what is written, regardless of any points or stars awarded. It is difficult to highlight this mood of a text because a document as a whole can contain both positive and negative statements. However, one can determine the text’s overall tendency relatively accurately by statistical and linguistic means.
Sentiment analyses are particularly suitable for marketing purposes to get opinions about ongoing campaigns in order to be able to react unerringly to them.
Text classification is a good way to understand the target group’s language – and making use of it for marketing purposes. No company can afford not to speak the same language as its customers.
The advantages of automatic text classification are obvious and they increase as the amount of information on the Internet expands. An additional push factor for text classification services is the fact that companies must always have an overview of any developments that are relevant to the market and that are emerging as trends on the web.
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Dieser Artikel wurde am 15.August 2019 von Jan Knupper geschrieben.
Jan Knupper is an independent author and writes for clickworker