Conventional approaches to calculating sentiment scores

[phonenumber]

The stay was nice but my room was cold and we had to wait for armenia b2b leads hour for the hotel staff to adjust the thermostat even though the hotel seem empty When we tried to call the reception to enquire they seemed impatient and rude

Stop-word removal
All superfluous words are eliminated so only named entities and words denoting emotions are kept.

The stay was nice My room cold and we had to wait for hour for the hotel staff to adjust the thermostat even though the hotel seem empty When we tried to call the reception to enquire they seemed impatient and rude

The resulting processed text now reads, nice room cold wait hour hotel staff reception impatient rude .

Since each word has a numerical equivalent in the ML model based on the scale of their negativity or positivity, the processed data gives you a score based on the total sentiment average. When calculated using the Lexicon method, if the word “nice” is assigned a score of 1 for positive, while “impatient” is assigned -.05 and rude -0.7, the resultant sentiment score for the review would be -1, which equates to negative.

There are multiple ways to calculate a sentiment score, the most common being the Lexicon method, which uses a 1:1 ratio to measure sentiment. However, when it comes to complex data collected from multiple sources such as social media listening or customer review forums, more advanced techniques are needed. Below is a breakdown of these methodologies.

Word count method
The simplest way to calculate the sentiment score is based on the lexicon or word-count method as in the example above. In this method, the number of negative sentiment occurrences is reduced from the positive occurrences.