tekstisuhtlust

Tekstisuhtlust, also known as text normalization or text cleaning, is the process of transforming raw text data into a standardized format. This process is crucial in natural language processing (NLP) and text mining tasks, as it ensures that the text data is consistent and free from noise. Tekstisuhtlust typically involves several steps, including tokenization, lowercasing, stopword removal, stemming, and lemmatization.

Tokenization is the process of breaking down text into individual words or tokens. This step is essential for further text processing tasks, as it allows for the analysis of individual words rather than entire sentences or paragraphs. Lowercasing is the process of converting all text to lowercase, which helps to reduce the number of unique words and improve the accuracy of text analysis.

Stopword removal is the process of removing common words that do not carry much meaning, such as "and," "the," and "is." These words are often removed to reduce the dimensionality of the text data and improve the efficiency of text analysis. Stemming and lemmatization are both processes of reducing words to their base or root form. Stemming involves removing suffixes from words, while lemmatization involves reducing words to their dictionary form.

Tekstisuhtlust is an essential step in text preprocessing, as it helps to ensure that the text data is consistent and free from noise. By transforming raw text data into a standardized format, tekstisuhtlust enables more accurate and efficient text analysis.