Data processing techniques used to calculate sentiment scores

[phonenumber]

Calculating a sentiment score for use in AI marketing depends armenia b2b leads on many data processing tasks done automatically by an ML model, such as large language models (LLM). These tasks include:

Tokenization
Tokenization is the process of separating the text into individual words. All punctuations are removed and the string of text is stripped down to blocks of words. For example:

[ The stay was nice but my room was cold and we had to wait for hours for the hotel staff to adjust the thermostat, even though the hotel seemed empty. When we tried to call the reception to enquire, they seemed impatient and rude ]

Text normalization
In this stage, all duplicate entries are removed from the data so there is no data anomaly. In this case, the text string remains unchanged as there is no redundancy.

[ The stay was nice but my room was cold and we had to wait for hours for the hotel staff to adjust the thermostat even though the hotel seemed empty When we tried to call the reception to enquire they seemed impatient and rude ]

Word stemming
Word stemming refers to the process of reducing a word to its root. In this example, the word “hours” and “seemed” are converted to “hour” and “seem”.