Context-Based Emotion Predictor: A Decision- Making Framework for Mobile Data

Table 1

List of acronyms used in the paper.

No	Description
Anx^1	is used to estimate the one text message score where present the score of all tokens. And represent total number of tokens, this formula calculates total score of one sentence by dividing no. of sentence token N
Anx^2	If there exist a negator in the target sentence, then this negation must be highlighted/bold and the sentiment score of text go to reverse by multiplying the score with −1 using following formula (sentence_socre × (−1))
Anx^3	is used to estimate the individual score in which represent the summary of text message for each individual user, where score divided by number of text messages by using 1/N in order to maintain the score within specified range
Anx^4	is used to estimate the individual score in which represent the summary of text message for each individual user, where score divided by number of text messages by 1/N in order to maintain the score within specified range
Anx^5	this formula is used to calculate the binary score in which present score of multiply user conversations, N represent total number of texts in i and j conversation and it calculate group session context-based emotion score
Anx^6	present overall score of individual where present individual score of users in indifferent session or group, N represent total number of texts in i, j conversation and its estimate cumulative context-based emotion summary (CCBES) of individual
Anx^7	present overall score of individual where present individual score of users in indifferent session or group, N represent total number of texts in i, j conversation and its estimate cumulative context-based emotion summary (CCBES) of individual
Anx^8	present average score of all user where is the conversations in any session, group, individual or in general, N represent total number of texts in i, j, …, k conversation and its calculate an average cumulative context-based emotion summary (CCBES) for all users