I have a a question regarding my current project, i am trying to do a lexicon based sentiment analysis on my data, where i calculate the sentiment score as following:

$$ Score = \frac{\sum_{i}{word_i}}{\mid words \mid} $$

So according to the score the word will be classified in either negative or positive. But i have also calculated for every word in the article its salience and frequency and would like to know if its possible to use them in my sentiment analysis formula above.

 words| salience| frequence
 sad    0.8       3
 happy  0.5       2

I am trying to do a lexicon based sentiment analysis on my data, where I calculate the sentiment score as follows:

$$ Score = \frac{\sum_{i}{word_i}}{\mid words \mid} $$

So according to the score the word will be classified in either negative or positive. But I have also calculated for every word in the article its salience and frequency:

Is it possible to use them in my sentiment analysis formula?

I have a a question regarding my current project, i am trying to do a lexicon based sentiment analysis on my data, where i calculate the sentiment score as following:

Score = word1+word2+word3 / |word|

So according to the score the word will be classified in either negative or positive. But i have also calculated for every word in the article its salience and frequency and would like to know if its possible to use them in my sentiment analysis formula above.

 words| salience| frequence
 sad    0.8       3
 happy  0.5       2

I have a a question regarding my current project, i am trying to do a lexicon based sentiment analysis on my data, where i calculate the sentiment score as following:

$$ Score = \frac{\sum_{i}{word_i}}{\mid words \mid} $$

So according to the score the word will be classified in either negative or positive. But i have also calculated for every word in the article its salience and frequency and would like to know if its possible to use them in my sentiment analysis formula above.

 words| salience| frequence
 sad    0.8       3
 happy  0.5       2